Results Study 1: Self-Improvement and Self-Acceptance as Mechanisms of Positive Personality Change
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1 Load packages
Show the code
library(renv)library(tidyverse)library(broom)library(labelled)library(psych)library(GPArotation)#library(devtools)#install_github("cran/multicon") # not on CRAN atmlibrary(multicon)library(correlation)library(careless)library(corrplot)library(lavaan)library(semTools)library(semPlot)library(knitr)library(ggdist)library(ggforce)library(cowplot)library(nortest)library(lmerTest)
Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some cases are empty and will be ignored:
16 41 91 317 381 382 414 444 505 606 705
summary(fit_cfa_neuro_curr, fit.measures =TRUE)
lavaan 0.6.15 ended normally after 34 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 36
Used Total
Number of observations 704 715
Number of missing patterns 4
Model Test User Model:
Test statistic 654.794
Degrees of freedom 54
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 3599.996
Degrees of freedom 66
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.830
Tucker-Lewis Index (TLI) 0.792
Robust Comparative Fit Index (CFI) 0.830
Robust Tucker-Lewis Index (TLI) 0.792
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -12392.351
Loglikelihood unrestricted model (H1) -12064.954
Akaike (AIC) 24856.701
Bayesian (BIC) 25020.745
Sample-size adjusted Bayesian (SABIC) 24906.437
Root Mean Square Error of Approximation:
RMSEA 0.126
90 Percent confidence interval - lower 0.117
90 Percent confidence interval - upper 0.134
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 1.000
Robust RMSEA 0.126
90 Percent confidence interval - lower 0.117
90 Percent confidence interval - upper 0.134
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 1.000
Standardized Root Mean Square Residual:
SRMR 0.060
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|)
neuro_curr1 =~
b05_04_ (lmb1) 0.889 0.044 20.156 0.000
b05_09_ (lmb2) 0.720 0.045 15.945 0.000
b05_14_ (lmb3) -0.831 0.047 -17.825 0.000
b05_19_ (lmb4) -0.598 0.040 -14.906 0.000
b05_24_ (lmb5) 0.803 0.045 17.928 0.000
b05_29_ (lmb6) 0.903 0.042 21.305 0.000
b05_34_ (lmb7) -0.810 0.041 -19.588 0.000
b05_39_ (lmb8) -0.923 0.044 -20.954 0.000
b05_44_ (lmb9) 0.776 0.043 18.009 0.000
b05_49_ (lm10) 0.599 0.046 12.942 0.000
b05_54_ (lm11) -0.949 0.045 -21.283 0.000
b05_59_ (lm12) -0.759 0.048 -15.789 0.000
Intercepts:
Estimate Std.Err z-value P(>|z|)
.bf05_04_1 (i1) 2.754 0.048 57.017 0.000
.bf05_09_1 2.926 0.047 62.162 0.000
.bf05_14_1 3.257 0.050 65.636 0.000
.bf05_19_1 3.639 0.041 87.961 0.000
.bf05_24_1 2.942 0.048 61.537 0.000
.bf05_29_1 2.908 0.047 61.917 0.000
.bf05_34_1 3.861 0.045 85.935 0.000
.bf05_39_1 3.383 0.048 70.033 0.000
.bf05_44_1 3.161 0.046 69.232 0.000
.bf05_49_1 2.366 0.047 50.510 0.000
.bf05_54_1 3.331 0.049 67.698 0.000
.bf05_59_1 3.151 0.050 63.190 0.000
neur_crr1 0.000
Variances:
Estimate Std.Err z-value P(>|z|)
.bf05_04_t1 0.852 0.051 16.673 0.000
.bf05_09_t1 1.041 0.059 17.648 0.000
.bf05_14_t1 1.042 0.060 17.251 0.000
.bf05_19_t1 0.848 0.048 17.841 0.000
.bf05_24_t1 0.963 0.056 17.288 0.000
.bf05_29_t1 0.737 0.046 16.091 0.000
.bf05_34_t1 0.766 0.046 16.816 0.000
.bf05_39_t1 0.791 0.050 15.897 0.000
.bf05_44_t1 0.865 0.051 16.946 0.000
.bf05_49_t1 1.184 0.065 18.086 0.000
.bf05_54_t1 0.804 0.051 15.845 0.000
.bf05_59_t1 1.174 0.067 17.579 0.000
neuro_curr1 1.000
Pre:
Here is a list of 15 personality traits. You might be high or low on any of these traits. Regardless of how high or low you are on these traits, we want to know how much you [want to] change. Please rate how much you would like to change your level of each of these traits, where 1 = I do not want to change and 5 = I want to change a lot.
Post:
Here is a list of 15 personality traits. During the study, you may have tried to change in some of these traits. We want to know how you think you changed in each trait. Please rate how much you changed your level of each of these traits during this study, where 1 = I am completely the same and 5 = I have changed a lot.
Self Acceptance
Pre:
Here is a list of 15 personality traits. You might be high or low on any of these traits. Regardless of how high or low you are on these traits, we want to know how much you accept yourself. Please rate how much you accept your level of each of these traits, where 1 = I completely accept myself and 5 = I want to accept myself more.
Post:
Here is a list of 15 personality traits. During the study, you may have tried to accept your level on some of these traits more. We want to know how much you think you accept yourself more because of this study. Please rate how much you accept your level of each of these traits more during this study, where 1 = I accept myself the same and 5 = I accept this about myself much more.
6.1 H1: Well-being - similarity correlations (H4 in paper)
All four psychological well-being indicators will be positively correlated with a greater similarity between current- and ideal self-ratings of personality.
To examine this at the level of overall profiles, we will compute the correlations between the psychological well-being indicators and the Fisher z transformed correlations between the facet- and item-level real-ideal self-profiles. To examine this at the level of individual traits, we will compute the correlation between psychological well-being indicators and the squared difference between current- and ideal-self rating for each Big Five trait and facet.
corrplot(cormat_profile, type ="lower", order ="original", tl.col ="black", tl.srt =10,addCoef.col ='black', number.cex =0.7, diag =FALSE) # also add numbers
Positive correlations of well-being indicators with profile similarity between current self and ideal self personality. Especially high correlation with self-esteem. High congruence of item-level and facet-level profile similarity.
corrplot(cormat_sqtraits, type ="lower", order ="original", tl.col ="black", tl.srt =10,addCoef.col ='black', number.cex =0.7, diag =FALSE) # also add numbers
Big Five facets
corrplot(cormat_sqfacets, type ="lower", order ="original", tl.col ="black", tl.srt =10,addCoef.col ='black', number.cex =0.6, diag =FALSE) # also add numbers
Here we see negative correlations of well-being indicators with squared trait- and facet-level mean-score differences between current self and ideal self personality. Especially pronounced negative correlations for neuroticism, extraversion, and conscientiousness (in that order).
6.2 H2: Well-being - latent change (H5 in paper)
Both groups will increase in all four psychological well-being indicators.
We will test the mean-level difference between baseline and follow up using a latent change model.
6.2.1 Life satisfaction
Fit model:
Show the code
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007# Fit the multiple indicator Univariate Latent Change Score modelmi_lcs_swls_hyp2 <-'swls_t1 =~ 1*sw06_01_t1 + lamb2*sw06_02_t1 + lamb3*sw06_03_t1 + lamb4*sw06_04_t1 # This specifies the measurement model for swls_t1 swls_t2 =~ 1*sw06_01_t2 + lamb2*sw06_02_t2 + lamb3*sw06_03_t2 + lamb4*sw06_04_t2 # This specifies the measurement model for swls_t2 with the equality constrained factor loadingsswls_t2 ~ 1*swls_t1 # This parameter regresses swls_t2 perfectly on swls_t1d_swls_1 =~ 1*swls_t2 # This defines the latent change score factor as measured perfectly by scores on swls_t2swls_t2 ~ 0*1 # This line constrains the intercept of swls_t2 to 0swls_t2 ~~ 0*swls_t2 # This fixes the variance of swls_t2 to 0d_swls_1 ~ 1 # This estimates the intercept of the change score swls_t1 ~ 1 # This estimates the intercept of swls_t1 d_swls_1 ~~ d_swls_1 # This estimates the variance of the change scores swls_t1 ~~ swls_t1 # This estimates the variance of the swls_t1 d_swls_1 ~ swls_t1 # This estimates the self-feedback parametersw06_01_t1 ~~ sw06_01_t2 # This allows residual covariance on indicator X1 across T1 and T2sw06_02_t1 ~~ sw06_02_t2 # This allows residual covariance on indicator X2 across T1 and T2sw06_03_t1 ~~ sw06_03_t2 # This allows residual covariance on indicator X3 across T1 and T2sw06_04_t1 ~~ sw06_04_t2 # This allows residual covariance on indicator X4 across T1 and T2sw06_01_t1 ~~ res1*sw06_01_t1 # This allows residual variance on indicator X1 at T1 sw06_02_t1 ~~ res2*sw06_02_t1 # This allows residual variance on indicator X2 at T1sw06_03_t1 ~~ res3*sw06_03_t1 # This allows residual variance on indicator X3 at T1sw06_04_t1 ~~ res4*sw06_04_t1 # This allows residual variance on indicator X4 at T1sw06_01_t2 ~~ res1*sw06_01_t2 # This allows residual variance on indicator X1 at T2 sw06_02_t2 ~~ res2*sw06_02_t2 # This allows residual variance on indicator X2 at T2 sw06_03_t2 ~~ res3*sw06_03_t2 # This allows residual variance on indicator X3 at T2sw06_04_t2 ~~ res4*sw06_04_t2 # This allows residual variance on indicator X4 at T2sw06_01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1sw06_02_t1 ~ m2*1 # This estimates the intercept of X2 at T1sw06_03_t1 ~ m3*1 # This estimates the intercept of X3 at T1sw06_04_t1 ~ m4*1 # This estimates the intercept of X4 at T1sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2sw06_02_t2 ~ m2*1 # This estimates the intercept of X2 at T2sw06_03_t2 ~ m3*1 # This estimates the intercept of X3 at T2sw06_04_t2 ~ m4*1 # This estimates the intercept of X4 at T2'fit_mi_lcs_swls_hyp2 <-lavaan(mi_lcs_swls_hyp2, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_swls_hyp2, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Significantly higher life satisfaction at the post test, b = 0.771, p = 0. Those with initially higher levels of life satisfaction (at T1) change to a lesser degree.
6.2.2 Meaning in life
Fit model:
Show the code
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007# Fit the multiple indicator Univariate Latent Change Score modelmi_lcs_meaning_hyp2 <-'meaning_t1 =~ 1*meaning_par1_t1 + lamb2*meaning_par2_t1 + lamb3*meaning_par3_t1 # This specifies the measurement model for meaning_t1 meaning_t2 =~ 1*meaning_par1_t2 + lamb2*meaning_par2_t2 + lamb3*meaning_par3_t2 # This specifies the measurement model for meaning_t2 with the equality constrained factor loadingsmeaning_t2 ~ 1*meaning_t1 # This parameter regresses meaning_t2 perfectly on meaning_t1d_meaning_1 =~ 1*meaning_t2 # This defines the latent change score factor as measured perfectly by scores on meaning_t2meaning_t2 ~ 0*1 # This line constrains the intercept of meaning_t2 to 0meaning_t2 ~~ 0*meaning_t2 # This fixes the variance of meaning_t2 to 0d_meaning_1 ~ 1 # This estimates the intercept of the change score meaning_t1 ~ 1 # This estimates the intercept of meaning_t1 d_meaning_1 ~~ d_meaning_1 # This estimates the variance of the change scores meaning_t1 ~~ meaning_t1 # This estimates the variance of the meaning_t1 d_meaning_1 ~ meaning_t1 # This estimates the self-feedback parametermeaning_par1_t1 ~~ meaning_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2meaning_par2_t1 ~~ meaning_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2meaning_par3_t1 ~~ meaning_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2meaning_par1_t1 ~~ res1*meaning_par1_t1 # This allows residual variance on indicator X1 at T1 meaning_par2_t1 ~~ res2*meaning_par2_t1 # This allows residual variance on indicator X2 at T1meaning_par3_t1 ~~ res3*meaning_par3_t1 # This allows residual variance on indicator X3 at T1meaning_par1_t2 ~~ res1*meaning_par1_t2 # This allows residual variance on indicator X1 at T2 meaning_par2_t2 ~~ res2*meaning_par2_t2 # This allows residual variance on indicator X2 at T2 meaning_par3_t2 ~~ res3*meaning_par3_t2 # This allows residual variance on indicator X3 at T2meaning_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1meaning_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1meaning_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2meaning_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2meaning_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2'fit_mi_lcs_meaning_hyp2 <-lavaan(mi_lcs_meaning_hyp2, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_meaning_hyp2, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Participants improved significantly in meaning in life across time, b = 1.38, p = 0.
6.2.3 Self-esteem
Fit model:
Show the code
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007# Fit the multiple indicator Univariate Latent Change Score modelmi_lcs_selfes_hyp2 <-'selfes_t1 =~ 1*selfes_par1_t1 + lamb2*selfes_par2_t1 + lamb3*selfes_par3_t1 # This specifies the measurement model for selfes_t1 selfes_t2 =~ 1*selfes_par1_t2 + lamb2*selfes_par2_t2 + lamb3*selfes_par3_t2 # This specifies the measurement model for selfes_t2 with the equality constrained factor loadingsselfes_t2 ~ 1*selfes_t1 # This parameter regresses selfes_t2 perfectly on selfes_t1d_selfes_1 =~ 1*selfes_t2 # This defines the latent change score factor as measured perfectly by scores on selfes_t2selfes_t2 ~ 0*1 # This line constrains the intercept of selfes_t2 to 0selfes_t2 ~~ 0*selfes_t2 # This fixes the variance of selfes_t2 to 0d_selfes_1 ~ 1 # This estimates the intercept of the change score selfes_t1 ~ 1 # This estimates the intercept of selfes_t1 d_selfes_1 ~~ d_selfes_1 # This estimates the variance of the change scores selfes_t1 ~~ selfes_t1 # This estimates the variance of the selfes_t1 d_selfes_1 ~ selfes_t1 # This estimates the self-feedback parameterselfes_par1_t1 ~~ selfes_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2selfes_par2_t1 ~~ selfes_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2selfes_par3_t1 ~~ selfes_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2selfes_par1_t1 ~~ res1*selfes_par1_t1 # This allows residual variance on indicator X1 at T1 selfes_par2_t1 ~~ res2*selfes_par2_t1 # This allows residual variance on indicator X2 at T1selfes_par3_t1 ~~ res3*selfes_par3_t1 # This allows residual variance on indicator X3 at T1selfes_par1_t2 ~~ res1*selfes_par1_t2 # This allows residual variance on indicator X1 at T2 selfes_par2_t2 ~~ res2*selfes_par2_t2 # This allows residual variance on indicator X2 at T2 selfes_par3_t2 ~~ res3*selfes_par3_t2 # This allows residual variance on indicator X3 at T2selfes_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1selfes_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1selfes_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2selfes_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2selfes_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2'fit_mi_lcs_selfes_hyp2 <-lavaan(mi_lcs_selfes_hyp2, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_selfes_hyp2, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Participants increased significantly in self-esteem between the two assessments, b = 1.084, p = 0.
6.2.4 Self concept clarity
Fit model:
Show the code
# Code snippets adapted from Kievit et al. (2018) -- CC-BY -- https://doi.org/10.1016/j.dcn.2017.11.007# Fit the multiple indicator Univariate Latent Change Score modelmi_lcs_concept_hyp2 <-'concept_t1 =~ 1*concept_par1_t1 + lamb2*concept_par2_t1 + lamb3*concept_par3_t1 # This specifies the measurement model for concept_t1 concept_t2 =~ 1*concept_par1_t2 + lamb2*concept_par2_t2 + lamb3*concept_par3_t2 # This specifies the measurement model for concept_t2 with the equality constrained factor loadingsconcept_t2 ~ 1*concept_t1 # This parameter regresses concept_t2 perfectly on concept_t1d_concept_1 =~ 1*concept_t2 # This defines the latent change score factor as measured perfectly by scores on concept_t2concept_t2 ~ 0*1 # This line constrains the intercept of concept_t2 to 0concept_t2 ~~ 0*concept_t2 # This fixes the variance of concept_t2 to 0d_concept_1 ~ 1 # This estimates the intercept of the change score concept_t1 ~ 1 # This estimates the intercept of concept_t1 d_concept_1 ~~ d_concept_1 # This estimates the variance of the change scores concept_t1 ~~ concept_t1 # This estimates the variance of the concept_t1 d_concept_1 ~ concept_t1 # This estimates the self-feedback parameterconcept_par1_t1 ~~ concept_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2concept_par2_t1 ~~ concept_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2concept_par3_t1 ~~ concept_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2concept_par1_t1 ~~ res1*concept_par1_t1 # This allows residual variance on indicator X1 at T1 concept_par2_t1 ~~ res2*concept_par2_t1 # This allows residual variance on indicator X2 at T1concept_par3_t1 ~~ res3*concept_par3_t1 # This allows residual variance on indicator X3 at T1concept_par1_t2 ~~ res1*concept_par1_t2 # This allows residual variance on indicator X1 at T2 concept_par2_t2 ~~ res2*concept_par2_t2 # This allows residual variance on indicator X2 at T2 concept_par3_t2 ~~ res3*concept_par3_t2 # This allows residual variance on indicator X3 at T2concept_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1concept_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1concept_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2concept_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2concept_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2'fit_mi_lcs_concept_hyp2 <-lavaan(mi_lcs_concept_hyp2, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_concept_hyp2, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Self concept clarity improved significantly across time, b = 1.159, p = 0.
6.3 H3: Distance between ideal- and current-self over time (H1 in paper)
The distance between ideal- and current-self will decrease in both groups.
We will use two strategies to test this hypothesis. First, we will compute the Fisher z-transformed profile correlation between current- and ideal-self and test whether it increased across assessments. Second, we will test whether the squared difference between current- and ideal-self ratings for each Big Five trait decreased across assessments. We will test mean-level differences in profile correlations and squared differences between baseline and follow up using repeated-measures t-test.
6.3.1 Profile similarity
Reshape to wide:
Show the code
# reshape to widedf_sbsa_wide_profdiff <- df_sbsa %>%arrange(pid, time) %>%select(pid, time, profile_corr_item_z, profile_corr_facet_z, ends_with("_sqdiff")) %>%pivot_wider(names_from = time,names_sep ="_t",values_from =-c(pid, time))
profile_df_plot <- df_sbsa %>%select(pid, time, profile_corr_item_z, profile_corr_facet_z) %>%pivot_longer(-c(pid, time), names_to ="itemfacet", values_to ="corr") %>%mutate(itemfacet2 =fct_recode(itemfacet, "Item-level"="profile_corr_item_z", "Facet-level"="profile_corr_facet_z"),itemfacet2 =fct_reorder(itemfacet2, corr, .desc = F))ggplot(profile_df_plot) +aes(x =as.factor(time), y = corr) +geom_boxplot() +geom_violin(fill =NA) +facet_wrap(vars(itemfacet2)) +labs(x ="Measurement Occasion", y ="Profile correlation", title ="H3: Distance between ideal- and current-self") +theme_bw()
Significantly higher profile correlations at the second measurement occasion, both for the item-level profile correlation and the facet-level profile correlations.
For some of the facets, the distribution look very similar and differences over time are perhaps driven by outliers.
6.4 H4: Change goals and change in personality (current / ideal) in self-improvement group
In the self-improvement group, there will be a correlation between change goals and change in current-self ratings but not change in ideal-self ratings.
We will test this one domain/facet at a time. We will use both general continuous change goal score as well as trait-specific change goals. To test this hypothesis, we will estimate the mean-level differences across time for both current and ideal trait ratings using latent change models and correlate change goals with the change variable from those models.
6.4.1.1 Extraversion - current-self: general change goals
Fit model:
Show the code
# adding correlation with manifest change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_extra_curr_hyp4 <-'extra_t1 =~ 1*extra_curr_par1_t1 + lamb2*extra_curr_par2_t1 + lamb3*extra_curr_par3_t1 # This specifies the measurement model for extra_t1 extra_t2 =~ 1*extra_curr_par1_t2 + lamb2*extra_curr_par2_t2 + lamb3*extra_curr_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadingsextra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0d_extra_1 ~ 1 # This estimates the intercept of the change score extra_t1 ~ 1 # This estimates the intercept of extra_t1 d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1 d_extra_1 ~ extra_t1 # This estimates the self-feedback parameterd_extra_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variableextra_curr_par1_t1 ~~ extra_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2extra_curr_par2_t1 ~~ extra_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2extra_curr_par3_t1 ~~ extra_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2extra_curr_par1_t1 ~~ res1*extra_curr_par1_t1 # This allows residual variance on indicator X1 at T1 extra_curr_par2_t1 ~~ res2*extra_curr_par2_t1 # This allows residual variance on indicator X2 at T1extra_curr_par3_t1 ~~ res3*extra_curr_par3_t1 # This allows residual variance on indicator X3 at T1extra_curr_par1_t2 ~~ res1*extra_curr_par1_t2 # This allows residual variance on indicator X1 at T2 extra_curr_par2_t2 ~~ res2*extra_curr_par2_t2 # This allows residual variance on indicator X2 at T2 extra_curr_par3_t2 ~~ res3*extra_curr_par3_t2 # This allows residual variance on indicator X3 at T2extra_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1extra_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1extra_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1extra_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2extra_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2extra_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_extra_curr_hyp4 <-lavaan(mi_lcs_extra_curr_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_extra_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sb06_01_t1 = general change goal):
The correlation of the general change goal with the extraversion change score (current-self) is significantly different from zero, r = 0.194, p = 0.015.
6.4.1.2 Extraversion - ideal-self: general change goals
Fit model:
Show the code
# adding correlation with manifest change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_extra_ideal_hyp4 <-'extra_t1 =~ 1*extra_ideal_par1_t1 + lamb2*extra_ideal_par2_t1 + lamb3*extra_ideal_par3_t1 # This specifies the measurement model for extra_t1 extra_t2 =~ 1*extra_ideal_par1_t2 + lamb2*extra_ideal_par2_t2 + lamb3*extra_ideal_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadingsextra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0d_extra_1 ~ 1 # This estimates the intercept of the change score extra_t1 ~ 1 # This estimates the intercept of extra_t1 d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1 d_extra_1 ~ extra_t1 # This estimates the self-feedback parameterd_extra_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variableextra_ideal_par1_t1 ~~ extra_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2extra_ideal_par2_t1 ~~ extra_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2extra_ideal_par3_t1 ~~ extra_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2extra_ideal_par1_t1 ~~ res1*extra_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 extra_ideal_par2_t1 ~~ res2*extra_ideal_par2_t1 # This allows residual variance on indicator X2 at T1extra_ideal_par3_t1 ~~ res3*extra_ideal_par3_t1 # This allows residual variance on indicator X3 at T1extra_ideal_par1_t2 ~~ res1*extra_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 extra_ideal_par2_t2 ~~ res2*extra_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 extra_ideal_par3_t2 ~~ res3*extra_ideal_par3_t2 # This allows residual variance on indicator X3 at T2extra_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1extra_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1extra_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1extra_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2extra_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2extra_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_extra_ideal_hyp4 <-lavaan(mi_lcs_extra_ideal_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_extra_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sb06_01_t1 = general change goal):
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_extra_curr_specif_hyp4 <-'extra_t1 =~ 1*extra_curr_par1_t1 + lamb2*extra_curr_par2_t1 + lamb3*extra_curr_par3_t1 # This specifies the measurement model for extra_t1 extra_t2 =~ 1*extra_curr_par1_t2 + lamb2*extra_curr_par2_t2 + lamb3*extra_curr_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadingsgoals =~ 1*sb07_01_t1 + sb07_02_t1 + sb07_03_t1 # latent change goal variable (three facets per trait)extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0d_extra_1 ~ 1 # This estimates the intercept of the change score extra_t1 ~ 1 # This estimates the intercept of extra_t1 d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1 d_extra_1 ~ extra_t1 # This estimates the self-feedback parameterd_extra_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0goals ~~ goals # This estimates the variance of the (latent) change goal variableextra_curr_par1_t1 ~~ extra_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2extra_curr_par2_t1 ~~ extra_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2extra_curr_par3_t1 ~~ extra_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2extra_curr_par1_t1 ~~ res1*extra_curr_par1_t1 # This allows residual variance on indicator X1 at T1 extra_curr_par2_t1 ~~ res2*extra_curr_par2_t1 # This allows residual variance on indicator X2 at T1extra_curr_par3_t1 ~~ res3*extra_curr_par3_t1 # This allows residual variance on indicator X3 at T1extra_curr_par1_t2 ~~ res1*extra_curr_par1_t2 # This allows residual variance on indicator X1 at T2 extra_curr_par2_t2 ~~ res2*extra_curr_par2_t2 # This allows residual variance on indicator X2 at T2 extra_curr_par3_t2 ~~ res3*extra_curr_par3_t2 # This allows residual variance on indicator X3 at T2extra_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1extra_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1extra_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1extra_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2extra_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2extra_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb07_01_t1 ~~ sb07_01_t1sb07_02_t1 ~~ sb07_02_t1sb07_03_t1 ~~ sb07_03_t1sb07_01_t1 ~ 1sb07_02_t1 ~ 1sb07_03_t1 ~ 1'fit_mi_lcs_extra_curr_specif_hyp4 <-lavaan(mi_lcs_extra_curr_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_extra_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific change goal):
Correlation of specific, facet-level change goals with extraversion change score (current-self) is not significantly different from zero, r = 0.05, p = 0.647.
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_extra_ideal_specif_hyp4 <-'extra_t1 =~ 1*extra_ideal_par1_t1 + lamb2*extra_ideal_par2_t1 + lamb3*extra_ideal_par3_t1 # This specifies the measurement model for extra_t1 extra_t2 =~ 1*extra_ideal_par1_t2 + lamb2*extra_ideal_par2_t2 + lamb3*extra_ideal_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadingsgoals =~ 1*sb07_01_t1 + sb07_02_t1 + sb07_03_t1 # latent change goal variable (three facets per trait)extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0d_extra_1 ~ 1 # This estimates the intercept of the change score extra_t1 ~ 1 # This estimates the intercept of extra_t1 d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1 d_extra_1 ~ extra_t1 # This estimates the self-feedback parameterd_extra_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0goals ~~ goals # This estimates the variance of the (latent) change goal variableextra_ideal_par1_t1 ~~ extra_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2extra_ideal_par2_t1 ~~ extra_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2extra_ideal_par3_t1 ~~ extra_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2extra_ideal_par1_t1 ~~ res1*extra_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 extra_ideal_par2_t1 ~~ res2*extra_ideal_par2_t1 # This allows residual variance on indicator X2 at T1extra_ideal_par3_t1 ~~ res3*extra_ideal_par3_t1 # This allows residual variance on indicator X3 at T1extra_ideal_par1_t2 ~~ res1*extra_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 extra_ideal_par2_t2 ~~ res2*extra_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 extra_ideal_par3_t2 ~~ res3*extra_ideal_par3_t2 # This allows residual variance on indicator X3 at T2extra_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1extra_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1extra_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1extra_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2extra_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2extra_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb07_01_t1 ~~ sb07_01_t1sb07_02_t1 ~~ sb07_02_t1sb07_03_t1 ~~ sb07_03_t1sb07_01_t1 ~ 1sb07_02_t1 ~ 1sb07_03_t1 ~ 1'fit_mi_lcs_extra_ideal_specif_hyp4 <-lavaan(mi_lcs_extra_ideal_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_extra_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific change goal):
Correlation of specific, facet-level change goals with extraversion change score (ideal-self) is not significantly different from zero, r = 0.178, p = 0.343.
6.4.1.5 Agreeableness - current-self: general change goals
Fit model:
Show the code
# adding correlation with manifest change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_agree_curr_hyp4 <-'agree_t1 =~ 1*agree_curr_par1_t1 + lamb2*agree_curr_par2_t1 + lamb3*agree_curr_par3_t1 # This specifies the measurement model for agree_t1agree_t2 =~ 1*agree_curr_par1_t2 + lamb2*agree_curr_par2_t2 + lamb3*agree_curr_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadingsagree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0d_agree_1 ~ 1 # This estimates the intercept of the change score agree_t1 ~ 1 # This estimates the intercept of agree_t1 d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1 d_agree_1 ~ agree_t1 # This estimates the self-feedback parameterd_agree_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variableagree_curr_par1_t1 ~~ agree_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2agree_curr_par2_t1 ~~ agree_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2agree_curr_par3_t1 ~~ agree_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2agree_curr_par1_t1 ~~ res1*agree_curr_par1_t1 # This allows residual variance on indicator X1 at T1 agree_curr_par2_t1 ~~ res2*agree_curr_par2_t1 # This allows residual variance on indicator X2 at T1agree_curr_par3_t1 ~~ res3*agree_curr_par3_t1 # This allows residual variance on indicator X3 at T1agree_curr_par1_t2 ~~ res1*agree_curr_par1_t2 # This allows residual variance on indicator X1 at T2 agree_curr_par2_t2 ~~ res2*agree_curr_par2_t2 # This allows residual variance on indicator X2 at T2 agree_curr_par3_t2 ~~ res3*agree_curr_par3_t2 # This allows residual variance on indicator X3 at T2agree_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1agree_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1agree_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1agree_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2agree_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2agree_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_agree_curr_hyp4 <-lavaan(mi_lcs_agree_curr_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_agree_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sb06_01_t1 = general change goal):
Correlation of general change goal with agreeableness change score (current-self) is not significantly different from zero, r = -0.069, p = 0.421.
6.4.1.6 Agreeableness - ideal-self: general change goals
Fit model:
Show the code
# adding correlation with manifest change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_agree_ideal_hyp4 <-'agree_t1 =~ 1*agree_ideal_par1_t1 + lamb2*agree_ideal_par2_t1 + lamb3*agree_ideal_par3_t1 # This specifies the measurement model for agree_t1 agree_t2 =~ 1*agree_ideal_par1_t2 + lamb2*agree_ideal_par2_t2 + lamb3*agree_ideal_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadingsagree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0d_agree_1 ~ 1 # This estimates the intercept of the change score agree_t1 ~ 1 # This estimates the intercept of agree_t1 d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1 d_agree_1 ~ agree_t1 # This estimates the self-feedback parameterd_agree_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variableagree_ideal_par1_t1 ~~ agree_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2agree_ideal_par2_t1 ~~ agree_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2agree_ideal_par3_t1 ~~ agree_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2agree_ideal_par1_t1 ~~ res1*agree_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 agree_ideal_par2_t1 ~~ res2*agree_ideal_par2_t1 # This allows residual variance on indicator X2 at T1agree_ideal_par3_t1 ~~ res3*agree_ideal_par3_t1 # This allows residual variance on indicator X3 at T1agree_ideal_par1_t2 ~~ res1*agree_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 agree_ideal_par2_t2 ~~ res2*agree_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 agree_ideal_par3_t2 ~~ res3*agree_ideal_par3_t2 # This allows residual variance on indicator X3 at T2agree_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1agree_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1agree_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1agree_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2agree_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2agree_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_agree_ideal_hyp4 <-lavaan(mi_lcs_agree_ideal_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_agree_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sb06_01_t1 = general change goal):
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_agree_curr_specif_hyp4 <-'agree_t1 =~ 1*agree_curr_par1_t1 + lamb2*agree_curr_par2_t1 + lamb3*agree_curr_par3_t1 # This specifies the measurement model for agree_t1agree_t2 =~ 1*agree_curr_par1_t2 + lamb2*agree_curr_par2_t2 + lamb3*agree_curr_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadingsgoals =~ 1*sb07_04_t1 + sb07_05_t1 + sb07_06_t1 # latent change goal variable (three facets per trait)agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0d_agree_1 ~ 1 # This estimates the intercept of the change score agree_t1 ~ 1 # This estimates the intercept of agree_t1 d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1 d_agree_1 ~ agree_t1 # This estimates the self-feedback parameterd_agree_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0goals ~~ goals # This estimates the variance of the (latent) change goal variableagree_curr_par1_t1 ~~ agree_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2agree_curr_par2_t1 ~~ agree_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2agree_curr_par3_t1 ~~ agree_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2agree_curr_par1_t1 ~~ res1*agree_curr_par1_t1 # This allows residual variance on indicator X1 at T1 agree_curr_par2_t1 ~~ res2*agree_curr_par2_t1 # This allows residual variance on indicator X2 at T1agree_curr_par3_t1 ~~ res3*agree_curr_par3_t1 # This allows residual variance on indicator X3 at T1agree_curr_par1_t2 ~~ res1*agree_curr_par1_t2 # This allows residual variance on indicator X1 at T2 agree_curr_par2_t2 ~~ res2*agree_curr_par2_t2 # This allows residual variance on indicator X2 at T2 agree_curr_par3_t2 ~~ res3*agree_curr_par3_t2 # This allows residual variance on indicator X3 at T2agree_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1agree_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1agree_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1agree_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2agree_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2agree_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb07_04_t1 ~~ sb07_04_t1sb07_05_t1 ~~ sb07_05_t1sb07_06_t1 ~~ sb07_06_t1sb07_04_t1 ~ 1sb07_05_t1 ~ 1sb07_06_t1 ~ 1'fit_mi_lcs_agree_curr_specif_hyp4 <-lavaan(mi_lcs_agree_curr_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_agree_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific change goal):
Correlation of specific, facet-level change goals with agreeableness change score (current-self) is not significantly different from zero, r = 0.03, p = 0.762.
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_agree_ideal_specif_hyp4 <-'agree_t1 =~ 1*agree_ideal_par1_t1 + lamb2*agree_ideal_par2_t1 + lamb3*agree_ideal_par3_t1 # This specifies the measurement model for agree_t1 agree_t2 =~ 1*agree_ideal_par1_t2 + lamb2*agree_ideal_par2_t2 + lamb3*agree_ideal_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadingsgoals =~ 1*sb07_04_t1 + sb07_05_t1 + sb07_06_t1 # latent change goal variable (three facets per trait)agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0d_agree_1 ~ 1 # This estimates the intercept of the change score agree_t1 ~ 1 # This estimates the intercept of agree_t1 d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1 d_agree_1 ~ agree_t1 # This estimates the self-feedback parameterd_agree_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0goals ~~ goals # This estimates the variance of the (latent) change goal variableagree_ideal_par1_t1 ~~ agree_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2agree_ideal_par2_t1 ~~ agree_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2agree_ideal_par3_t1 ~~ agree_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2agree_ideal_par1_t1 ~~ res1*agree_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 agree_ideal_par2_t1 ~~ res2*agree_ideal_par2_t1 # This allows residual variance on indicator X2 at T1agree_ideal_par3_t1 ~~ res3*agree_ideal_par3_t1 # This allows residual variance on indicator X3 at T1agree_ideal_par1_t2 ~~ res1*agree_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 agree_ideal_par2_t2 ~~ res2*agree_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 agree_ideal_par3_t2 ~~ res3*agree_ideal_par3_t2 # This allows residual variance on indicator X3 at T2agree_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1agree_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1agree_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1agree_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2agree_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2agree_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb07_04_t1 ~~ sb07_04_t1sb07_05_t1 ~~ sb07_05_t1sb07_06_t1 ~~ sb07_06_t1sb07_04_t1 ~ 1sb07_05_t1 ~ 1sb07_06_t1 ~ 1'fit_mi_lcs_agree_ideal_specif_hyp4 <-lavaan(mi_lcs_agree_ideal_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_agree_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific change goal):
Correlation of specific, facet-level change goals with agreeableness change score (ideal-self) is not significantly different from zero, r = -0.123, p = 0.171.
6.4.1.9 Conscientiousness - current-self: general change goals
Fit model:
Show the code
# adding correlation with manifest change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_consc_curr_hyp4 <-'consc_t1 =~ 1*consc_curr_par1_t1 + lamb2*consc_curr_par2_t1 + lamb3*consc_curr_par3_t1 # This specifies the measurement model for consc_t1 consc_t2 =~ 1*consc_curr_par1_t2 + lamb2*consc_curr_par2_t2 + lamb3*consc_curr_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadingsconsc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0d_consc_1 ~ 1 # This estimates the intercept of the change score consc_t1 ~ 1 # This estimates the intercept of consc_t1 d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1 d_consc_1 ~ consc_t1 # This estimates the self-feedback parameterd_consc_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variableconsc_curr_par1_t1 ~~ consc_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2consc_curr_par2_t1 ~~ consc_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2consc_curr_par3_t1 ~~ consc_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2consc_curr_par1_t1 ~~ res1*consc_curr_par1_t1 # This allows residual variance on indicator X1 at T1 consc_curr_par2_t1 ~~ res2*consc_curr_par2_t1 # This allows residual variance on indicator X2 at T1consc_curr_par3_t1 ~~ res3*consc_curr_par3_t1 # This allows residual variance on indicator X3 at T1consc_curr_par1_t2 ~~ res1*consc_curr_par1_t2 # This allows residual variance on indicator X1 at T2 consc_curr_par2_t2 ~~ res2*consc_curr_par2_t2 # This allows residual variance on indicator X2 at T2 consc_curr_par3_t2 ~~ res3*consc_curr_par3_t2 # This allows residual variance on indicator X3 at T2consc_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1consc_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1consc_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1consc_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2consc_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2consc_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_consc_curr_hyp4 <-lavaan(mi_lcs_consc_curr_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_consc_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sb06_01_t1 = general change goal):
Correlation of general change goal with conscientiousness change score (current-self) is not significantly different from zero, r = 0.022, p = 0.756.
6.4.1.10 Conscientiousness - ideal-self: general change goals
Fit model:
Show the code
# adding correlation with manifest change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_consc_ideal_hyp4 <-'consc_t1 =~ 1*consc_ideal_par1_t1 + lamb2*consc_ideal_par2_t1 + lamb3*consc_ideal_par3_t1 # This specifies the measurement model for consc_t1consc_t2 =~ 1*consc_ideal_par1_t2 + lamb2*consc_ideal_par2_t2 + lamb3*consc_ideal_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadingsconsc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0d_consc_1 ~ 1 # This estimates the intercept of the change score consc_t1 ~ 1 # This estimates the intercept of consc_t1 d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1 d_consc_1 ~ consc_t1 # This estimates the self-feedback parameterd_consc_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variableconsc_ideal_par1_t1 ~~ consc_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2consc_ideal_par2_t1 ~~ consc_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2consc_ideal_par3_t1 ~~ consc_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2consc_ideal_par1_t1 ~~ res1*consc_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 consc_ideal_par2_t1 ~~ res2*consc_ideal_par2_t1 # This allows residual variance on indicator X2 at T1consc_ideal_par3_t1 ~~ res3*consc_ideal_par3_t1 # This allows residual variance on indicator X3 at T1consc_ideal_par1_t2 ~~ res1*consc_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 consc_ideal_par2_t2 ~~ res2*consc_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 consc_ideal_par3_t2 ~~ res3*consc_ideal_par3_t2 # This allows residual variance on indicator X3 at T2consc_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1consc_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1consc_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1consc_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2consc_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2consc_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_consc_ideal_hyp4 <-lavaan(mi_lcs_consc_ideal_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_consc_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sb06_01_t1 = general change goal):
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_consc_curr_specif_hyp4 <-'consc_t1 =~ 1*consc_curr_par1_t1 + lamb2*consc_curr_par2_t1 + lamb3*consc_curr_par3_t1 # This specifies the measurement model for consc_t1 consc_t2 =~ 1*consc_curr_par1_t2 + lamb2*consc_curr_par2_t2 + lamb3*consc_curr_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadingsgoals =~ 1*sb07_07_t1 + sb07_08_t1 + sb07_09_t1 # latent change goal variable (three facets per trait)consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0d_consc_1 ~ 1 # This estimates the intercept of the change score consc_t1 ~ 1 # This estimates the intercept of consc_t1 d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1 d_consc_1 ~ consc_t1 # This estimates the self-feedback parameterd_consc_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0goals ~~ goals # This estimates the variance of the (latent) change goal variableconsc_curr_par1_t1 ~~ consc_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2consc_curr_par2_t1 ~~ consc_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2consc_curr_par3_t1 ~~ consc_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2consc_curr_par1_t1 ~~ res1*consc_curr_par1_t1 # This allows residual variance on indicator X1 at T1 consc_curr_par2_t1 ~~ res2*consc_curr_par2_t1 # This allows residual variance on indicator X2 at T1consc_curr_par3_t1 ~~ res3*consc_curr_par3_t1 # This allows residual variance on indicator X3 at T1consc_curr_par1_t2 ~~ res1*consc_curr_par1_t2 # This allows residual variance on indicator X1 at T2 consc_curr_par2_t2 ~~ res2*consc_curr_par2_t2 # This allows residual variance on indicator X2 at T2 consc_curr_par3_t2 ~~ res3*consc_curr_par3_t2 # This allows residual variance on indicator X3 at T2consc_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1consc_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1consc_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1consc_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2consc_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2consc_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb07_07_t1 ~~ sb07_07_t1sb07_08_t1 ~~ sb07_08_t1sb07_09_t1 ~~ sb07_09_t1sb07_07_t1 ~ 1sb07_08_t1 ~ 1sb07_09_t1 ~ 1'fit_mi_lcs_consc_curr_specif_hyp4 <-lavaan(mi_lcs_consc_curr_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_consc_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific change goal):
Correlation of specific, facet-level change goals with conscientiousness change score (current-self) is not significantly different from zero, r = -0.073, p = 0.405.
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_consc_ideal_specif_hyp4 <-'consc_t1 =~ 1*consc_ideal_par1_t1 + lamb2*consc_ideal_par2_t1 + lamb3*consc_ideal_par3_t1 # This specifies the measurement model for consc_t1consc_t2 =~ 1*consc_ideal_par1_t2 + lamb2*consc_ideal_par2_t2 + lamb3*consc_ideal_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadingsgoals =~ 1*sb07_07_t1 + sb07_08_t1 + sb07_09_t1 # latent change goal variable (three facets per trait)consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0d_consc_1 ~ 1 # This estimates the intercept of the change score consc_t1 ~ 1 # This estimates the intercept of consc_t1 d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1 d_consc_1 ~ consc_t1 # This estimates the self-feedback parameterd_consc_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0goals ~~ goals # This estimates the variance of the (latent) change goal variableconsc_ideal_par1_t1 ~~ consc_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2consc_ideal_par2_t1 ~~ consc_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2consc_ideal_par3_t1 ~~ consc_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2consc_ideal_par1_t1 ~~ res1*consc_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 consc_ideal_par2_t1 ~~ res2*consc_ideal_par2_t1 # This allows residual variance on indicator X2 at T1consc_ideal_par3_t1 ~~ res3*consc_ideal_par3_t1 # This allows residual variance on indicator X3 at T1consc_ideal_par1_t2 ~~ res1*consc_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 consc_ideal_par2_t2 ~~ res2*consc_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 consc_ideal_par3_t2 ~~ res3*consc_ideal_par3_t2 # This allows residual variance on indicator X3 at T2consc_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1consc_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1consc_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1consc_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2consc_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2consc_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb07_07_t1 ~~ sb07_07_t1sb07_08_t1 ~~ sb07_08_t1sb07_09_t1 ~~ sb07_09_t1sb07_07_t1 ~ 1sb07_08_t1 ~ 1sb07_09_t1 ~ 1'fit_mi_lcs_consc_ideal_specif_hyp4 <-lavaan(mi_lcs_consc_ideal_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_consc_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific change goal):
Correlation of specific, facet-level change goals with conscientiousness change score (ideal-self) is not significantly different from zero, r = -0.027, p = 0.732.
6.4.1.13 Neuroticism - current-self: general change goals
Fit model:
Show the code
# adding correlation with manifest change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_neuro_curr_hyp4 <-'neuro_t1 =~ 1*neuro_curr_par1_t1 + lamb2*neuro_curr_par2_t1 + lamb3*neuro_curr_par3_t1 # This specifies the measurement model for neuro_t1 neuro_t2 =~ 1*neuro_curr_par1_t2 + lamb2*neuro_curr_par2_t2 + lamb3*neuro_curr_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadingsneuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0d_neuro_1 ~ 1 # This estimates the intercept of the change score neuro_t1 ~ 1 # This estimates the intercept of neuro_t1 d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1 d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameterd_neuro_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variableneuro_curr_par1_t1 ~~ neuro_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2neuro_curr_par2_t1 ~~ neuro_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2neuro_curr_par3_t1 ~~ neuro_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2neuro_curr_par1_t1 ~~ res1*neuro_curr_par1_t1 # This allows residual variance on indicator X1 at T1 neuro_curr_par2_t1 ~~ res2*neuro_curr_par2_t1 # This allows residual variance on indicator X2 at T1neuro_curr_par3_t1 ~~ res3*neuro_curr_par3_t1 # This allows residual variance on indicator X3 at T1neuro_curr_par1_t2 ~~ res1*neuro_curr_par1_t2 # This allows residual variance on indicator X1 at T2 neuro_curr_par2_t2 ~~ res2*neuro_curr_par2_t2 # This allows residual variance on indicator X2 at T2 neuro_curr_par3_t2 ~~ res3*neuro_curr_par3_t2 # This allows residual variance on indicator X3 at T2neuro_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1neuro_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1neuro_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1neuro_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2neuro_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2neuro_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_neuro_curr_hyp4 <-lavaan(mi_lcs_neuro_curr_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_neuro_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sb06_01_t1 = general change goal):
Correlation of general change goal with neuroticism change score (current-self) is not significantly different from zero, r = -0.098, p = 0.13.
6.4.1.14 Neuroticism - ideal-self: general change goals
Fit model:
Show the code
# adding correlation with manifest change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_neuro_ideal_hyp4 <-'neuro_t1 =~ 1*neuro_ideal_par1_t1 + lamb2*neuro_ideal_par2_t1 + lamb3*neuro_ideal_par3_t1 # This specifies the measurement model for neuro_t1 neuro_t2 =~ 1*neuro_ideal_par1_t2 + lamb2*neuro_ideal_par2_t2 + lamb3*neuro_ideal_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadingsneuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0d_neuro_1 ~ 1 # This estimates the intercept of the change score neuro_t1 ~ 1 # This estimates the intercept of neuro_t1 d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1 d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameterd_neuro_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variableneuro_ideal_par1_t1 ~~ neuro_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2neuro_ideal_par1_t1 ~~ res1*neuro_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 neuro_ideal_par2_t1 ~~ res2*neuro_ideal_par2_t1 # This allows residual variance on indicator X2 at T1neuro_ideal_par3_t1 ~~ res3*neuro_ideal_par3_t1 # This allows residual variance on indicator X3 at T1neuro_ideal_par1_t2 ~~ res1*neuro_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 neuro_ideal_par2_t2 ~~ res2*neuro_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 neuro_ideal_par3_t2 ~~ res3*neuro_ideal_par3_t2 # This allows residual variance on indicator X3 at T2neuro_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1neuro_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1neuro_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1neuro_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2neuro_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2neuro_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_neuro_ideal_hyp4 <-lavaan(mi_lcs_neuro_ideal_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_neuro_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sb06_01_t1 = general change goal):
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_neuro_curr_specif_hyp4 <-'neuro_t1 =~ 1*neuro_curr_par1_t1 + lamb2*neuro_curr_par2_t1 + lamb3*neuro_curr_par3_t1 # This specifies the measurement model for neuro_t1 neuro_t2 =~ 1*neuro_curr_par1_t2 + lamb2*neuro_curr_par2_t2 + lamb3*neuro_curr_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadingsgoals =~ 1*sb07_10_t1 + sb07_11_t1 + sb07_12_t1 # latent change goal variable (three facets per trait)neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0d_neuro_1 ~ 1 # This estimates the intercept of the change score neuro_t1 ~ 1 # This estimates the intercept of neuro_t1 d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1 d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameterd_neuro_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0goals ~~ goals # This estimates the variance of the (latent) change goal variableneuro_curr_par1_t1 ~~ neuro_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2neuro_curr_par2_t1 ~~ neuro_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2neuro_curr_par3_t1 ~~ neuro_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2neuro_curr_par1_t1 ~~ res1*neuro_curr_par1_t1 # This allows residual variance on indicator X1 at T1 neuro_curr_par2_t1 ~~ res2*neuro_curr_par2_t1 # This allows residual variance on indicator X2 at T1neuro_curr_par3_t1 ~~ res3*neuro_curr_par3_t1 # This allows residual variance on indicator X3 at T1neuro_curr_par1_t2 ~~ res1*neuro_curr_par1_t2 # This allows residual variance on indicator X1 at T2 neuro_curr_par2_t2 ~~ res2*neuro_curr_par2_t2 # This allows residual variance on indicator X2 at T2 neuro_curr_par3_t2 ~~ res3*neuro_curr_par3_t2 # This allows residual variance on indicator X3 at T2neuro_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1neuro_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1neuro_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1neuro_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2neuro_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2neuro_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb07_10_t1 ~~ sb07_10_t1sb07_11_t1 ~~ sb07_11_t1sb07_12_t1 ~~ sb07_12_t1sb07_10_t1 ~ 1sb07_11_t1 ~ 1sb07_12_t1 ~ 1'fit_mi_lcs_neuro_curr_specif_hyp4 <-lavaan(mi_lcs_neuro_curr_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_neuro_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific change goal):
The correlation of specific, facet-level change goals with neuroticism change score (current-self) is significantly different from zero, r = 0.237, p = 0.01.
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_neuro_ideal_specif_hyp4 <-'neuro_t1 =~ 1*neuro_ideal_par1_t1 + lamb2*neuro_ideal_par2_t1 + lamb3*neuro_ideal_par3_t1 # This specifies the measurement model for neuro_t1 neuro_t2 =~ 1*neuro_ideal_par1_t2 + lamb2*neuro_ideal_par2_t2 + lamb3*neuro_ideal_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadingsgoals =~ 1*sb07_10_t1 + sb07_11_t1 + sb07_12_t1 # latent change goal variable (three facets per trait)neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0d_neuro_1 ~ 1 # This estimates the intercept of the change score neuro_t1 ~ 1 # This estimates the intercept of neuro_t1 d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1 d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameterd_neuro_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0goals ~~ goals # This estimates the variance of the (latent) change goal variableneuro_ideal_par1_t1 ~~ neuro_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2neuro_ideal_par1_t1 ~~ res1*neuro_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 neuro_ideal_par2_t1 ~~ res2*neuro_ideal_par2_t1 # This allows residual variance on indicator X2 at T1neuro_ideal_par3_t1 ~~ res3*neuro_ideal_par3_t1 # This allows residual variance on indicator X3 at T1neuro_ideal_par1_t2 ~~ res1*neuro_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 neuro_ideal_par2_t2 ~~ res2*neuro_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 neuro_ideal_par3_t2 ~~ res3*neuro_ideal_par3_t2 # This allows residual variance on indicator X3 at T2neuro_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1neuro_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1neuro_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1neuro_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2neuro_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2neuro_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb07_10_t1 ~~ sb07_10_t1sb07_11_t1 ~~ sb07_11_t1sb07_12_t1 ~~ sb07_12_t1sb07_10_t1 ~ 1sb07_11_t1 ~ 1sb07_12_t1 ~ 1'fit_mi_lcs_neuro_ideal_specif_hyp4 <-lavaan(mi_lcs_neuro_ideal_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_neuro_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific change goal):
Correlation of specific, facet-level change goals with neuroticism change score (ideal-self) is not significantly different from zero, r = 0.006, p = 0.934.
6.4.1.17 Openness - current-self: general change goals
Fit model:
Show the code
# adding correlation with manifest change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_openn_curr_hyp4 <-'openn_t1 =~ 1*openn_curr_par1_t1 + lamb2*openn_curr_par2_t1 + lamb3*openn_curr_par3_t1 # This specifies the measurement model for openn_t1openn_t2 =~ 1*openn_curr_par1_t2 + lamb2*openn_curr_par2_t2 + lamb3*openn_curr_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadingsopenn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0d_openn_1 ~ 1 # This estimates the intercept of the change score openn_t1 ~ 1 # This estimates the intercept of openn_t1 d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1 d_openn_1 ~ openn_t1 # This estimates the self-feedback parameterd_openn_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variableopenn_curr_par1_t1 ~~ openn_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2openn_curr_par2_t1 ~~ openn_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2openn_curr_par3_t1 ~~ openn_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2openn_curr_par1_t1 ~~ res1*openn_curr_par1_t1 # This allows residual variance on indicator X1 at T1 openn_curr_par2_t1 ~~ res2*openn_curr_par2_t1 # This allows residual variance on indicator X2 at T1openn_curr_par3_t1 ~~ res3*openn_curr_par3_t1 # This allows residual variance on indicator X3 at T1openn_curr_par1_t2 ~~ res1*openn_curr_par1_t2 # This allows residual variance on indicator X1 at T2 openn_curr_par2_t2 ~~ res2*openn_curr_par2_t2 # This allows residual variance on indicator X2 at T2 openn_curr_par3_t2 ~~ res3*openn_curr_par3_t2 # This allows residual variance on indicator X3 at T2openn_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1openn_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1openn_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1openn_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2openn_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2openn_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_openn_curr_hyp4 <-lavaan(mi_lcs_openn_curr_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_openn_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sb06_01_t1 = general change goal):
Correlation of general change goal with openness change score (current-self) is not significantly different from zero, r = -0.068, p = 0.474.
6.4.1.18 Openness - ideal-self: general change goals
Fit model:
Show the code
# adding correlation with manifest change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_openn_ideal_hyp4 <-'openn_t1 =~ 1*openn_ideal_par1_t1 + lamb2*openn_ideal_par2_t1 + lamb3*openn_ideal_par3_t1 # This specifies the measurement model for openn_t1 openn_t2 =~ 1*openn_ideal_par1_t2 + lamb2*openn_ideal_par2_t2 + lamb3*openn_ideal_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadingsopenn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0d_openn_1 ~ 1 # This estimates the intercept of the change score openn_t1 ~ 1 # This estimates the intercept of openn_t1 d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1 d_openn_1 ~ openn_t1 # This estimates the self-feedback parameterd_openn_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variableopenn_ideal_par1_t1 ~~ openn_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2openn_ideal_par2_t1 ~~ openn_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2openn_ideal_par3_t1 ~~ openn_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2openn_ideal_par1_t1 ~~ res1*openn_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 openn_ideal_par2_t1 ~~ res2*openn_ideal_par2_t1 # This allows residual variance on indicator X2 at T1openn_ideal_par3_t1 ~~ res3*openn_ideal_par3_t1 # This allows residual variance on indicator X3 at T1openn_ideal_par1_t2 ~~ res1*openn_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 openn_ideal_par2_t2 ~~ res2*openn_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 openn_ideal_par3_t2 ~~ res3*openn_ideal_par3_t2 # This allows residual variance on indicator X3 at T2openn_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1openn_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1openn_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1openn_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2openn_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2openn_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_openn_ideal_hyp4 <-lavaan(mi_lcs_openn_ideal_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_openn_ideal_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sb06_01_t1 = general change goal):
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_openn_curr_specif_hyp4 <-'openn_t1 =~ 1*openn_curr_par1_t1 + lamb2*openn_curr_par2_t1 + lamb3*openn_curr_par3_t1 # This specifies the measurement model for openn_t1openn_t2 =~ 1*openn_curr_par1_t2 + lamb2*openn_curr_par2_t2 + lamb3*openn_curr_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadingsgoals =~ 1*sb07_13_t1 + sb07_14_t1 + sb07_15_t1 # latent change goal variable (three facets per trait)openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0d_openn_1 ~ 1 # This estimates the intercept of the change score openn_t1 ~ 1 # This estimates the intercept of openn_t1 d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1 d_openn_1 ~ openn_t1 # This estimates the self-feedback parameterd_openn_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0goals ~~ goals # This estimates the variance of the (latent) change goal variableopenn_curr_par1_t1 ~~ openn_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2openn_curr_par2_t1 ~~ openn_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2openn_curr_par3_t1 ~~ openn_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2openn_curr_par1_t1 ~~ res1*openn_curr_par1_t1 # This allows residual variance on indicator X1 at T1 openn_curr_par2_t1 ~~ res2*openn_curr_par2_t1 # This allows residual variance on indicator X2 at T1openn_curr_par3_t1 ~~ res3*openn_curr_par3_t1 # This allows residual variance on indicator X3 at T1openn_curr_par1_t2 ~~ res1*openn_curr_par1_t2 # This allows residual variance on indicator X1 at T2 openn_curr_par2_t2 ~~ res2*openn_curr_par2_t2 # This allows residual variance on indicator X2 at T2 openn_curr_par3_t2 ~~ res3*openn_curr_par3_t2 # This allows residual variance on indicator X3 at T2openn_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1openn_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1openn_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1openn_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2openn_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2openn_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb07_13_t1 ~~ sb07_13_t1sb07_14_t1 ~~ sb07_14_t1sb07_15_t1 ~~ sb07_15_t1sb07_13_t1 ~ 1sb07_14_t1 ~ 1sb07_15_t1 ~ 1'fit_mi_lcs_openn_curr_specif_hyp4 <-lavaan(mi_lcs_openn_curr_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_openn_curr_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific change goal):
The correlation of specific, facet-level change goals with openness change score (current-self) is significantly different from zero, r = 0.225, p = 0.025.
# adding correlation with latent (made up of the three facets) change goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_openn_ideal_specif_hyp4 <-'openn_t1 =~ 1*openn_ideal_par1_t1 + lamb2*openn_ideal_par2_t1 + lamb3*openn_ideal_par3_t1 # This specifies the measurement model for openn_t1 openn_t2 =~ 1*openn_ideal_par1_t2 + lamb2*openn_ideal_par2_t2 + lamb3*openn_ideal_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadingsgoals =~ 1*sb07_13_t1 + sb07_14_t1 + sb07_15_t1 # latent change goal variable (three facets per trait)openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0d_openn_1 ~ 1 # This estimates the intercept of the change score openn_t1 ~ 1 # This estimates the intercept of openn_t1 d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1 d_openn_1 ~ openn_t1 # This estimates the self-feedback parameterd_openn_1 ~~ goals # estimates the covariance/correlation with the (latent) change goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) change goal variable to 0goals ~~ goals # This estimates the variance of the (latent) change goal variableopenn_ideal_par1_t1 ~~ openn_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2openn_ideal_par2_t1 ~~ openn_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2openn_ideal_par3_t1 ~~ openn_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2openn_ideal_par1_t1 ~~ res1*openn_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 openn_ideal_par2_t1 ~~ res2*openn_ideal_par2_t1 # This allows residual variance on indicator X2 at T1openn_ideal_par3_t1 ~~ res3*openn_ideal_par3_t1 # This allows residual variance on indicator X3 at T1openn_ideal_par1_t2 ~~ res1*openn_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 openn_ideal_par2_t2 ~~ res2*openn_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 openn_ideal_par3_t2 ~~ res3*openn_ideal_par3_t2 # This allows residual variance on indicator X3 at T2openn_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1openn_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1openn_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1openn_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2openn_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2openn_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb07_13_t1 ~~ sb07_13_t1sb07_14_t1 ~~ sb07_14_t1sb07_15_t1 ~~ sb07_15_t1sb07_13_t1 ~ 1sb07_14_t1 ~ 1sb07_15_t1 ~ 1'fit_mi_lcs_openn_ideal_specif_hyp4 <-lavaan(mi_lcs_openn_ideal_specif_hyp4, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_openn_ideal_specif_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific change goal):
Correlation of specific, facet-level change goals with openness change score (ideal-self) is not significantly different from zero, r = 0.138, p = 0.166.
6.4.2 Big Five facets
Run models for all facets with a template & loop:
Show the code
# create template:facet_template <-'facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0d_facet_1 ~ 1 # This estimates the intercept of the change score facet_t1 ~ 1 # This estimates the intercept of facet_t1 d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1 d_facet_1 ~ facet_t1 # This estimates the self-feedback parameterd_facet_1 ~~ ind_goal # estimates the covariance/correlation with change goal variableind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1 ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2 ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2 ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2ind_goal ~~ ind_goalind_goal ~ 1'# loop across 15 facetsfor (i in6:length(b5_vars)) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# loop across 2 BFI versions (combined pre&post current/ideal)for (j in5:length(bfi_versions)) { items =paste0(bfi_versions[[j]], item_nrs)# loop across 2 different goal operationalizations (sb06_01_t1 & sb07_XX_t1)for (k in1:2) {if (k==1) { goal_op ="sb06_01_t1" } else{ goal_op =paste0("sb07_", str_pad(i-5, 2, pad ="0"), "_t1") } template_filled <-str_replace_all(facet_template, c("facet"= short_name,"ind1"= items[1], "ind2"= items[2], "ind3"= items[3], "ind4"= items[4],"ind_goal"= goal_op)) facet_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sb, estimator='mlr', fixed.x=FALSE, missing='fiml')# save to environmentif (k==1) {eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[j], 6), "_hyp4")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[j], 6), "_hyp4")), facet_model_fit)) } else{eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[j], 6), "_specif_hyp4")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[j], 6), "_specif_hyp4")), facet_model_fit)) } } }}
6.4.2.1 Sociability - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with sociability change score (current-self) is not significantly different from zero, r = -0.077, p = 0.332.
The correlation of specific, facet-level change goals with the anxiety change score (ideal-self) is (barely) significantly different from zero, r = 0.238, p = 0.035.
6.4.2.5 Assertiveness - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with assertiveness change score (current-self) is not significantly different from zero, r = 0.047, p = 0.668.
Correlation of specific, facet-level change goals with assertiveness change score (ideal-self) is not significantly different from zero, r = 0.062, p = 0.489.
6.4.2.9 Energy - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with energy change score (current-self) is not significantly different from zero, r = 0.096, p = 0.26.
6.4.2.12 Energy - ideal-self: specific, facet-level change goals
Results summary (sb07_xx_t1 = trait/facet specific change goal):
Correlation of specific, facet-level change goals with energy change score (ideal-self) is not significantly different from zero, r = 0.082, p = 0.413.
6.4.2.13 Compassion - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with compassion change score (current-self) is not significantly different from zero, r = -0.014, p = 0.924.
Correlation of specific, facet-level change goals with compassion change score (ideal-self) is not significantly different from zero, r = -0.058, p = 0.556.
6.4.2.17 Respectfulness - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with respectfulness change score (current-self) is not significantly different from zero, r = 0.03, p = 0.761.
The correlation of specific, facet-level change goals with the respectfulness change score (ideal-self) is (barely) significantly different from zero, r = -0.173, p = 0.049.
6.4.2.21 Trust - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with trust change score (current-self) is not significantly different from zero, r = -0.007, p = 0.944.
Correlation of specific, facet-level change goals with organization change score (current-self) is not significantly different from zero, r = 0.145, p = 0.136.
Correlation of specific, facet-level change goals with organization change score (ideal-self) is not significantly different from zero, r = 0.113, p = 0.209.
6.4.2.29 Productiveness - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with productiveness change score (current-self) is not significantly different from zero, r = 0.114, p = 0.218.
Correlation of specific, facet-level change goals with productiveness change score (ideal-self) is not significantly different from zero, r = 0.01, p = 0.903.
6.4.2.33 Responsibility - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with responsibility change score (current-self) is not significantly different from zero, r = -0.194, p = 0.068.
Correlation of specific, facet-level change goals with responsibility change score (ideal-self) is not significantly different from zero, r = 0.026, p = 0.801.
6.4.2.37 Anxiety - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
The correlation of specific, facet-level change goals with the anxiety change score (current-self) is significantly different from zero, r = -0.287, p = 0.002.
Correlation of specific, facet-level change goals with anxiety change score (ideal-self) is not significantly different from zero, r = 0.037, p = 0.65.
6.4.2.41 Depression - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
The correlation of specific, facet-level change goals with the depression change score (current-self) is significantly different from zero, r = -0.311, p = 0.002.
Correlation of specific, facet-level change goals with the depression change score (ideal-self) is not significantly different from zero, r = -0.071, p = 0.328.
6.4.2.45 Volatility - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with volatility change score (current-self) is not significantly different from zero, r = 0.028, p = 0.748.
Correlation of specific, facet-level change goals with volatility change score (ideal-self) is not significantly different from zero, r = -0.012, p = 0.902.
6.4.2.49 Curiosity - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with curiosity change score (current-self) is not significantly different from zero, r = -0.116, p = 0.429.
Correlation of specific, facet-level change goals with curiosity change score (ideal-self) is not significantly different from zero, r = 0.232, p = 0.868.
6.4.2.53 Aesthetic - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
(here there were some convergence problems with the standard model that the loop tried to fit)
mi_lcs_aesth_curr_hyp4 <-'aesth_t1 =~ 1*bf05_05_t1 + lamb2*bf05_20_t1 + lamb3*bf05_35_t1 + lamb4*bf05_50_t1 # This specifies the measurement model for aesth at T1aesth_t2 =~ 1*bf05_05_t2 + lamb2*bf05_20_t2 + lamb3*bf05_35_t2 + lamb4*bf05_50_t2 # This specifies the measurement model for aesth at T2 (with equality constraints)aesth_t2 ~ 1*aesth_t1 # This parameter regresses aesth_t2 perfectly on aesth_t1d_aesth_1 =~ 1*aesth_t2 # This defines the latent change score factor as measured perfectly by scores on aesth_t2aesth_t2 ~ 0*1 # This line constrains the intercept of aesth_t2 to 0aesth_t2 ~~ 0*aesth_t2 # This fixes the variance of aesth_t2 to 0d_aesth_1 ~ 1 # This estimates the intercept of the change score aesth_t1 ~ 1 # This estimates the intercept of aesth_t1 d_aesth_1 ~~ d_aesth_1 # This estimates the variance of the change scores aesth_t1 ~~ aesth_t1 # This estimates the variance of aesth_t1 d_aesth_1 ~ aesth_t1 # This estimates the self-feedback parameterd_aesth_1 ~~ sb06_01_t1 # estimates the covariance/correlation with change goal variablebf05_05_t1 ~~ bf05_05_t2 # This allows residual covariance on indicator X1 across T1 and T2bf05_20_t1 ~~ bf05_20_t2 # This allows residual covariance on indicator X2 across T1 and T2bf05_35_t1 ~~ bf05_35_t2 # This allows residual covariance on indicator X3 across T1 and T2bf05_50_t1 ~~ bf05_50_t2 # This allows residual covariance on indicator X4 across T1 and T2bf05_05_t1 ~~ res1*bf05_05_t1 # This allows residual variance on indicator X1 at T1 bf05_20_t1 ~~ res2*bf05_20_t1 # This allows residual variance on indicator X2 at T1bf05_35_t1 ~~ res3*bf05_35_t1 # This allows residual variance on indicator X3 at T1bf05_50_t1 ~~ res4*bf05_50_t1 # This allows residual variance on indicator X4 at T1bf05_05_t2 ~~ res1*bf05_05_t2 # This allows residual variance on indicator X1 at T2 bf05_20_t2 ~~ res2*bf05_20_t2 # This allows residual variance on indicator X2 at T2 bf05_35_t2 ~~ res3*bf05_35_t2 # This allows residual variance on indicator X3 at T2bf05_50_t2 ~~ res4*bf05_50_t2 # This allows residual variance on indicator X4 at T2bf05_05_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1bf05_20_t1 ~ m2*1 # This estimates the intercept of X2 at T1bf05_35_t1 ~ m3*1 # This estimates the intercept of X3 at T1bf05_50_t1 ~ m4*1 # This estimates the intercept of X4 at T1bf05_05_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2bf05_20_t2 ~ m2*1 # This estimates the intercept of X2 at T2bf05_35_t2 ~ m3*1 # This estimates the intercept of X3 at T2bf05_50_t2 ~ m4*1 # This estimates the intercept of X4 at T2sb06_01_t1 ~~ sb06_01_t1sb06_01_t1 ~ 1'fit_mi_lcs_aesth_curr_hyp4 <-lavaan(mi_lcs_aesth_curr_hyp4, data=df_sbsa_wide_pers_sb %>%filter(!is.na(bf05_05_t1) &!is.na(bf05_05_t2)), estimator='mlr', fixed.x=FALSE, missing="fiml")# This model did not converge properly with FIML and missings. No problem with complete data, though. summary(fit_mi_lcs_aesth_curr_hyp4, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Correlation of specific, facet-level change goals with aesthetic change score (current-self) is not significantly different from zero, r = 0.144, p = 0.052.
Correlation of specific, facet-level change goals with aesthetic change score (ideal-self) is not significantly different from zero, r = 0.237, p = 0.137.
6.4.2.57 Imagination - current-self: general change goals
Results summary (sb06_01_t1 = general change goal):
Correlation of specific, facet-level change goals with imagination change score (current-self) is not significantly different from zero, r = 0.07, p = 0.459.
Correlation of specific, facet-level change goals with imagination change score (ideal-self) is not significantly different from zero, r = -0.022, p = 0.774.
Results summary across the Big Five traits: covariance of the latent change score and change goal(s)
kable(df_table_hyp4[1:20, ], digits =3)
trait
ref
goal
estimate
std.all
statistic
p.value
extraversion
current
general
0.064
0.194
2.423
0.015
extraversion
ideal
general
0.060
0.208
2.245
0.025
extraversion
current
specific
0.010
0.050
0.458
0.647
extraversion
ideal
specific
0.032
0.178
0.948
0.343
agreeableness
current
general
-0.014
-0.069
-0.805
0.421
agreeableness
ideal
general
0.016
0.063
0.742
0.458
agreeableness
current
specific
0.005
0.030
0.303
0.762
agreeableness
ideal
specific
-0.028
-0.123
-1.368
0.171
conscientiousness
current
general
0.007
0.022
0.311
0.756
conscientiousness
ideal
general
0.010
0.036
0.444
0.657
conscientiousness
current
specific
-0.028
-0.073
-0.832
0.405
conscientiousness
ideal
specific
-0.009
-0.027
-0.343
0.732
neuroticism
current
general
-0.045
-0.098
-1.514
0.130
neuroticism
ideal
general
-0.029
-0.100
-1.267
0.205
neuroticism
current
specific
0.121
0.237
2.591
0.010
neuroticism
ideal
specific
0.002
0.006
0.083
0.934
openness
current
general
-0.013
-0.068
-0.717
0.474
openness
ideal
general
-0.014
-0.061
-0.622
0.534
openness
current
specific
0.026
0.225
2.242
0.025
openness
ideal
specific
0.020
0.138
1.386
0.166
Three covariances/correlations that significantly differ from zero:
- Changes in current-level and ideal-level extraversion covary with the general change goal.
- Changes in current-level neuroticism covary with the specific trait goals (latent factor of the three N facets).
- Changes in current-level openness covary with the specific trait goals (latent factor of the three O facets).
Results summary across the Big Five facets: covariance of the latent change score and change goal(s)
kable(df_table_hyp4[21:80, ], digits =3)
trait
ref
goal
estimate
std.all
statistic
p.value
sociability
current
general
0.088
0.180
2.159
0.031
sociability
ideal
general
0.047
0.207
1.727
0.084
sociability
current
specific
-0.045
-0.077
-0.970
0.332
sociability
ideal
specific
0.067
0.238
2.111
0.035
assertiveness
current
general
0.034
0.147
1.136
0.256
assertiveness
ideal
general
0.050
0.248
2.400
0.016
assertiveness
current
specific
0.013
0.047
0.428
0.668
assertiveness
ideal
specific
0.015
0.062
0.692
0.489
energy
current
general
-0.018
-0.087
-0.933
0.351
energy
ideal
general
-0.036
-0.146
-1.440
0.150
energy
current
specific
0.025
0.096
1.127
0.260
energy
ideal
specific
0.025
0.082
0.818
0.413
compassion
current
general
0.038
0.172
1.390
0.165
compassion
ideal
general
0.024
0.058
0.580
0.562
compassion
current
specific
-0.004
-0.014
-0.095
0.924
compassion
ideal
specific
-0.034
-0.058
-0.588
0.556
respectfulness
current
general
-0.035
-0.122
-1.351
0.177
respectfulness
ideal
general
0.007
0.031
0.341
0.733
respectfulness
current
specific
0.011
0.030
0.304
0.761
respectfulness
ideal
specific
-0.057
-0.173
-1.971
0.049
trust
current
general
-0.006
-0.021
-0.213
0.832
trust
ideal
general
-0.016
-0.070
-0.697
0.486
trust
current
specific
-0.003
-0.007
-0.071
0.944
trust
ideal
specific
0.018
0.057
0.573
0.566
organization
current
general
-0.074
-0.157
-1.822
0.068
organization
ideal
general
-0.016
-0.053
-0.577
0.564
organization
current
specific
0.103
0.145
1.492
0.136
organization
ideal
specific
0.050
0.113
1.256
0.209
productiveness
current
general
0.023
0.066
0.739
0.460
productiveness
ideal
general
0.009
0.036
0.381
0.703
productiveness
current
specific
0.052
0.114
1.233
0.218
productiveness
ideal
specific
0.003
0.010
0.122
0.903
responsibility
current
general
-0.038
-0.187
-1.757
0.079
responsibility
ideal
general
0.019
0.074
0.700
0.484
responsibility
current
specific
-0.061
-0.194
-1.826
0.068
responsibility
ideal
specific
0.010
0.026
0.252
0.801
anxiety
current
general
0.070
0.136
1.763
0.078
anxiety
ideal
general
0.003
0.018
0.185
0.853
anxiety
current
specific
-0.233
-0.287
-3.083
0.002
anxiety
ideal
specific
0.010
0.037
0.454
0.650
depression
current
general
0.025
0.070
0.956
0.339
depression
ideal
general
0.035
0.124
1.401
0.161
depression
current
specific
-0.182
-0.311
-3.059
0.002
depression
ideal
specific
-0.032
-0.071
-0.978
0.328
volatility
current
general
0.012
0.026
0.308
0.758
volatility
ideal
general
-0.068
-0.231
-2.172
0.030
volatility
current
specific
0.017
0.028
0.321
0.748
volatility
ideal
specific
-0.005
-0.012
-0.123
0.902
curiosity
current
general
-0.002
-0.010
-0.070
0.944
curiosity
ideal
general
0.009
0.358
0.368
0.713
curiosity
current
specific
-0.028
-0.116
-0.790
0.429
curiosity
ideal
specific
0.005
0.232
0.166
0.868
aesthetic
current
general
-0.002
-0.082
-0.927
0.354
aesthetic
ideal
general
-0.005
-0.073
-0.725
0.469
aesthetic
current
specific
0.002
0.144
1.940
0.052
aesthetic
ideal
specific
0.024
0.237
1.488
0.137
imagination
current
general
0.011
0.033
0.348
0.728
imagination
ideal
general
0.015
0.043
0.509
0.611
imagination
current
specific
0.034
0.070
0.740
0.459
imagination
ideal
specific
-0.011
-0.022
-0.288
0.774
Looking at the facets, we see five covariances that significantly differ from zero:
- For sociability, changes in the current-level covary with the general change goal and changes in the ideal-level with the specific facet change goal (both effects barely significant).
- For assertiveness, changes in the ideal-level covary with the general change goal. - Matching the effects from neuroticism above, we find that changes in current-level anxiety and depression covary with the respective specific facet change goal. Changes in ideal-level volatility covary with the general change goal.
- Further, changes in ideal-level respectfulness covary with the specific facet change goal (small effect that is barely significant; in the right direction, though -> minus sign is because a reverse-keyed item was used as reference indicator).
6.5 H5: Acceptance goals and change in personality (current / ideal) in self-acceptance group
In the self-acceptance group, there will be a correlation between acceptance goals and change in ideal-self ratings but not change in current-self ratings.
We will test this one domain/facet at a time. We will use both general continuous change goal score as well as trait-specific change goals. To test this hypothesis, we will estimate the mean-level difference across time for both current and ideal trait ratings using latent change models and correlate change goals with the change variable from those models.
6.5.1.1 Extraversion - current-self: general acceptance goals
Fit model:
Show the code
# adding correlation with manifest acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_extra_curr_hyp5 <-'extra_t1 =~ 1*extra_curr_par1_t1 + lamb2*extra_curr_par2_t1 + lamb3*extra_curr_par3_t1 # This specifies the measurement model for extra_t1 extra_t2 =~ 1*extra_curr_par1_t2 + lamb2*extra_curr_par2_t2 + lamb3*extra_curr_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadingsextra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0d_extra_1 ~ 1 # This estimates the intercept of the change score extra_t1 ~ 1 # This estimates the intercept of extra_t1 d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1 d_extra_1 ~ extra_t1 # This estimates the self-feedback parameterd_extra_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variableextra_curr_par1_t1 ~~ extra_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2extra_curr_par2_t1 ~~ extra_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2extra_curr_par3_t1 ~~ extra_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2extra_curr_par1_t1 ~~ res1*extra_curr_par1_t1 # This allows residual variance on indicator X1 at T1 extra_curr_par2_t1 ~~ res2*extra_curr_par2_t1 # This allows residual variance on indicator X2 at T1extra_curr_par3_t1 ~~ res3*extra_curr_par3_t1 # This allows residual variance on indicator X3 at T1extra_curr_par1_t2 ~~ res1*extra_curr_par1_t2 # This allows residual variance on indicator X1 at T2 extra_curr_par2_t2 ~~ res2*extra_curr_par2_t2 # This allows residual variance on indicator X2 at T2 extra_curr_par3_t2 ~~ res3*extra_curr_par3_t2 # This allows residual variance on indicator X3 at T2extra_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1extra_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1extra_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1extra_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2extra_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2extra_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa06_01_t1 ~~ sa06_01_t1sa06_01_t1 ~ 1'fit_mi_lcs_extra_curr_hyp5 <-lavaan(mi_lcs_extra_curr_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_extra_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sa06_01_t1 = general acceptance goal):
The correlation of general acceptance goal with the extraversion change score (current-self) is significantly different from zero, r = 0.193, p = 0.001.
6.5.1.2 Extraversion - ideal-self: general acceptance goals
Fit model:
Show the code
# adding correlation with manifest acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_extra_ideal_hyp5 <-'extra_t1 =~ 1*extra_ideal_par1_t1 + lamb2*extra_ideal_par2_t1 + lamb3*extra_ideal_par3_t1 # This specifies the measurement model for extra_t1 extra_t2 =~ 1*extra_ideal_par1_t2 + lamb2*extra_ideal_par2_t2 + lamb3*extra_ideal_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadingsextra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0d_extra_1 ~ 1 # This estimates the intercept of the change score extra_t1 ~ 1 # This estimates the intercept of extra_t1 d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1 d_extra_1 ~ extra_t1 # This estimates the self-feedback parameterd_extra_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variableextra_ideal_par1_t1 ~~ extra_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2extra_ideal_par2_t1 ~~ extra_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2extra_ideal_par3_t1 ~~ extra_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2extra_ideal_par1_t1 ~~ res1*extra_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 extra_ideal_par2_t1 ~~ res2*extra_ideal_par2_t1 # This allows residual variance on indicator X2 at T1extra_ideal_par3_t1 ~~ res3*extra_ideal_par3_t1 # This allows residual variance on indicator X3 at T1extra_ideal_par1_t2 ~~ res1*extra_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 extra_ideal_par2_t2 ~~ res2*extra_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 extra_ideal_par3_t2 ~~ res3*extra_ideal_par3_t2 # This allows residual variance on indicator X3 at T2extra_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1extra_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1extra_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1extra_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2extra_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2extra_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa06_01_t1 ~~ sa06_01_t1sa06_01_t1 ~ 1'fit_mi_lcs_extra_ideal_hyp5 <-lavaan(mi_lcs_extra_ideal_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_extra_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sa06_01_t1 = general acceptance goal):
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_extra_curr_specif_hyp5 <-'extra_t1 =~ 1*extra_curr_par1_t1 + lamb2*extra_curr_par2_t1 + lamb3*extra_curr_par3_t1 # This specifies the measurement model for extra_t1 extra_t2 =~ 1*extra_curr_par1_t2 + lamb2*extra_curr_par2_t2 + lamb3*extra_curr_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadingsgoals =~ 1*sa07_01_t1 + sa07_02_t1 + sa07_03_t1 # latent acceptance goal variable (three facets per trait)extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0d_extra_1 ~ 1 # This estimates the intercept of the change score extra_t1 ~ 1 # This estimates the intercept of extra_t1 d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1 d_extra_1 ~ extra_t1 # This estimates the self-feedback parameterd_extra_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0goals ~~ goals # This estimates the variance of the (latent) acceptance goal variableextra_curr_par1_t1 ~~ extra_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2extra_curr_par2_t1 ~~ extra_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2extra_curr_par3_t1 ~~ extra_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2extra_curr_par1_t1 ~~ res1*extra_curr_par1_t1 # This allows residual variance on indicator X1 at T1 extra_curr_par2_t1 ~~ res2*extra_curr_par2_t1 # This allows residual variance on indicator X2 at T1extra_curr_par3_t1 ~~ res3*extra_curr_par3_t1 # This allows residual variance on indicator X3 at T1extra_curr_par1_t2 ~~ res1*extra_curr_par1_t2 # This allows residual variance on indicator X1 at T2 extra_curr_par2_t2 ~~ res2*extra_curr_par2_t2 # This allows residual variance on indicator X2 at T2 extra_curr_par3_t2 ~~ res3*extra_curr_par3_t2 # This allows residual variance on indicator X3 at T2extra_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1extra_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1extra_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1extra_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2extra_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2extra_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa07_01_t1 ~~ sa07_01_t1sa07_02_t1 ~~ sa07_02_t1sa07_03_t1 ~~ sa07_03_t1sa07_01_t1 ~ 1sa07_02_t1 ~ 1sa07_03_t1 ~ 1'fit_mi_lcs_extra_curr_specif_hyp5 <-lavaan(mi_lcs_extra_curr_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_extra_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific acceptance goal):
Correlation of specific, facet-level acceptance goals with extraversion change score (current-self) is not significantly different from zero, r = 0.052, p = 0.62.
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_extra_ideal_specif_hyp5 <-'extra_t1 =~ 1*extra_ideal_par1_t1 + lamb2*extra_ideal_par2_t1 + lamb3*extra_ideal_par3_t1 # This specifies the measurement model for extra_t1 extra_t2 =~ 1*extra_ideal_par1_t2 + lamb2*extra_ideal_par2_t2 + lamb3*extra_ideal_par3_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadingsgoals =~ 1*sa07_01_t1 + sa07_02_t1 + sa07_03_t1 # latent acceptance goal variable (three facets per trait)extra_t2 ~ 1*extra_t1 # This parameter regresses extra_t2 perfectly on extra_t1d_extra_1 =~ 1*extra_t2 # This defines the latent change score factor as measured perfectly by scores on extra_t2extra_t2 ~ 0*1 # This line constrains the intercept of extra_t2 to 0extra_t2 ~~ 0*extra_t2 # This fixes the variance of extra_t2 to 0d_extra_1 ~ 1 # This estimates the intercept of the change score extra_t1 ~ 1 # This estimates the intercept of extra_t1 d_extra_1 ~~ d_extra_1 # This estimates the variance of the change scores extra_t1 ~~ extra_t1 # This estimates the variance of the extra_t1 d_extra_1 ~ extra_t1 # This estimates the self-feedback parameterd_extra_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0goals ~~ goals # This estimates the variance of the (latent) acceptance goal variableextra_ideal_par1_t1 ~~ extra_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2extra_ideal_par2_t1 ~~ extra_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2extra_ideal_par3_t1 ~~ extra_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2extra_ideal_par1_t1 ~~ res1*extra_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 extra_ideal_par2_t1 ~~ res2*extra_ideal_par2_t1 # This allows residual variance on indicator X2 at T1extra_ideal_par3_t1 ~~ res3*extra_ideal_par3_t1 # This allows residual variance on indicator X3 at T1extra_ideal_par1_t2 ~~ res1*extra_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 extra_ideal_par2_t2 ~~ res2*extra_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 extra_ideal_par3_t2 ~~ res3*extra_ideal_par3_t2 # This allows residual variance on indicator X3 at T2extra_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1extra_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1extra_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1extra_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2extra_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2extra_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa07_01_t1 ~~ sa07_01_t1sa07_02_t1 ~~ sa07_02_t1sa07_03_t1 ~~ sa07_03_t1sa07_01_t1 ~ 1sa07_02_t1 ~ 1sa07_03_t1 ~ 1'fit_mi_lcs_extra_ideal_specif_hyp5 <-lavaan(mi_lcs_extra_ideal_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_extra_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific acceptance goal):
Correlation of specific, facet-level acceptance goals with extraversion change score (ideal-self) is not significantly different from zero, r = -0.097, p = 0.365.
6.5.1.5 Agreeableness - current-self: general acceptance goals
Fit model:
Show the code
# adding correlation with manifest acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_agree_curr_hyp5 <-'agree_t1 =~ 1*agree_curr_par1_t1 + lamb2*agree_curr_par2_t1 + lamb3*agree_curr_par3_t1 # This specifies the measurement model for agree_t1agree_t2 =~ 1*agree_curr_par1_t2 + lamb2*agree_curr_par2_t2 + lamb3*agree_curr_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadingsagree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0d_agree_1 ~ 1 # This estimates the intercept of the change score agree_t1 ~ 1 # This estimates the intercept of agree_t1 d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1 d_agree_1 ~ agree_t1 # This estimates the self-feedback parameterd_agree_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variableagree_curr_par1_t1 ~~ agree_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2agree_curr_par2_t1 ~~ agree_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2agree_curr_par3_t1 ~~ agree_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2agree_curr_par1_t1 ~~ res1*agree_curr_par1_t1 # This allows residual variance on indicator X1 at T1 agree_curr_par2_t1 ~~ res2*agree_curr_par2_t1 # This allows residual variance on indicator X2 at T1agree_curr_par3_t1 ~~ res3*agree_curr_par3_t1 # This allows residual variance on indicator X3 at T1agree_curr_par1_t2 ~~ res1*agree_curr_par1_t2 # This allows residual variance on indicator X1 at T2 agree_curr_par2_t2 ~~ res2*agree_curr_par2_t2 # This allows residual variance on indicator X2 at T2 agree_curr_par3_t2 ~~ res3*agree_curr_par3_t2 # This allows residual variance on indicator X3 at T2agree_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1agree_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1agree_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1agree_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2agree_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2agree_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa06_01_t1 ~~ sa06_01_t1sa06_01_t1 ~ 1'fit_mi_lcs_agree_curr_hyp5 <-lavaan(mi_lcs_agree_curr_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_agree_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of general acceptance goal with agreeableness change score (current-self) is not significantly different from zero, r = 0.145, p = 0.129.
6.5.1.6 Agreeableness - ideal-self: general acceptance goals
Fit model:
Show the code
# adding correlation with manifest acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_agree_ideal_hyp5 <-'agree_t1 =~ 1*agree_ideal_par1_t1 + lamb2*agree_ideal_par2_t1 + lamb3*agree_ideal_par3_t1 # This specifies the measurement model for agree_t1 agree_t2 =~ 1*agree_ideal_par1_t2 + lamb2*agree_ideal_par2_t2 + lamb3*agree_ideal_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadingsagree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0d_agree_1 ~ 1 # This estimates the intercept of the change score agree_t1 ~ 1 # This estimates the intercept of agree_t1 d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1 d_agree_1 ~ agree_t1 # This estimates the self-feedback parameterd_agree_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variableagree_ideal_par1_t1 ~~ agree_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2agree_ideal_par2_t1 ~~ agree_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2agree_ideal_par3_t1 ~~ agree_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2agree_ideal_par1_t1 ~~ res1*agree_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 agree_ideal_par2_t1 ~~ res2*agree_ideal_par2_t1 # This allows residual variance on indicator X2 at T1agree_ideal_par3_t1 ~~ res3*agree_ideal_par3_t1 # This allows residual variance on indicator X3 at T1agree_ideal_par1_t2 ~~ res1*agree_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 agree_ideal_par2_t2 ~~ res2*agree_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 agree_ideal_par3_t2 ~~ res3*agree_ideal_par3_t2 # This allows residual variance on indicator X3 at T2agree_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1agree_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1agree_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1agree_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2agree_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2agree_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa06_01_t1 ~~ sa06_01_t1sa06_01_t1 ~ 1'fit_mi_lcs_agree_ideal_hyp5 <-lavaan(mi_lcs_agree_ideal_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_agree_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sa06_01_t1 = general acceptance goal):
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_agree_curr_specif_hyp5 <-'agree_t1 =~ 1*agree_curr_par1_t1 + lamb2*agree_curr_par2_t1 + lamb3*agree_curr_par3_t1 # This specifies the measurement model for agree_t1agree_t2 =~ 1*agree_curr_par1_t2 + lamb2*agree_curr_par2_t2 + lamb3*agree_curr_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadingsgoals =~ 1*sa07_04_t1 + sa07_05_t1 + sa07_06_t1 # latent acceptance goal variable (three facets per trait)agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0d_agree_1 ~ 1 # This estimates the intercept of the change score agree_t1 ~ 1 # This estimates the intercept of agree_t1 d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1 d_agree_1 ~ agree_t1 # This estimates the self-feedback parameterd_agree_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0goals ~~ goals # This estimates the variance of the (latent) acceptance goal variableagree_curr_par1_t1 ~~ agree_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2agree_curr_par2_t1 ~~ agree_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2agree_curr_par3_t1 ~~ agree_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2agree_curr_par1_t1 ~~ res1*agree_curr_par1_t1 # This allows residual variance on indicator X1 at T1 agree_curr_par2_t1 ~~ res2*agree_curr_par2_t1 # This allows residual variance on indicator X2 at T1agree_curr_par3_t1 ~~ res3*agree_curr_par3_t1 # This allows residual variance on indicator X3 at T1agree_curr_par1_t2 ~~ res1*agree_curr_par1_t2 # This allows residual variance on indicator X1 at T2 agree_curr_par2_t2 ~~ res2*agree_curr_par2_t2 # This allows residual variance on indicator X2 at T2 agree_curr_par3_t2 ~~ res3*agree_curr_par3_t2 # This allows residual variance on indicator X3 at T2agree_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1agree_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1agree_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1agree_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2agree_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2agree_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa07_04_t1 ~~ sa07_04_t1sa07_05_t1 ~~ sa07_05_t1sa07_06_t1 ~~ sa07_06_t1sa07_04_t1 ~ 1sa07_05_t1 ~ 1sa07_06_t1 ~ 1'fit_mi_lcs_agree_curr_specif_hyp5 <-lavaan(mi_lcs_agree_curr_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_agree_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific acceptance goal):
Correlation of specific, facet-level acceptance goals with agreeableness change score (current-self) is not significantly different from zero, r = 0.035, p = 0.748.
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_agree_ideal_specif_hyp5 <-'agree_t1 =~ 1*agree_ideal_par1_t1 + lamb2*agree_ideal_par2_t1 + lamb3*agree_ideal_par3_t1 # This specifies the measurement model for agree_t1 agree_t2 =~ 1*agree_ideal_par1_t2 + lamb2*agree_ideal_par2_t2 + lamb3*agree_ideal_par3_t2 # This specifies the measurement model for agree_t2 with the equality constrained factor loadingsgoals =~ 1*sa07_04_t1 + sa07_05_t1 + sa07_06_t1 # latent acceptance goal variable (three facets per trait)agree_t2 ~ 1*agree_t1 # This parameter regresses agree_t2 perfectly on agree_t1d_agree_1 =~ 1*agree_t2 # This defines the latent change score factor as measured perfectly by scores on agree_t2agree_t2 ~ 0*1 # This line constrains the intercept of agree_t2 to 0agree_t2 ~~ 0*agree_t2 # This fixes the variance of agree_t2 to 0d_agree_1 ~ 1 # This estimates the intercept of the change score agree_t1 ~ 1 # This estimates the intercept of agree_t1 d_agree_1 ~~ d_agree_1 # This estimates the variance of the change scores agree_t1 ~~ agree_t1 # This estimates the variance of the agree_t1 d_agree_1 ~ agree_t1 # This estimates the self-feedback parameterd_agree_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0goals ~~ goals # This estimates the variance of the (latent) acceptance goal variableagree_ideal_par1_t1 ~~ agree_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2agree_ideal_par2_t1 ~~ agree_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2agree_ideal_par3_t1 ~~ agree_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2agree_ideal_par1_t1 ~~ res1*agree_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 agree_ideal_par2_t1 ~~ res2*agree_ideal_par2_t1 # This allows residual variance on indicator X2 at T1agree_ideal_par3_t1 ~~ res3*agree_ideal_par3_t1 # This allows residual variance on indicator X3 at T1agree_ideal_par1_t2 ~~ res1*agree_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 agree_ideal_par2_t2 ~~ res2*agree_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 agree_ideal_par3_t2 ~~ res3*agree_ideal_par3_t2 # This allows residual variance on indicator X3 at T2agree_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1agree_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1agree_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1agree_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2agree_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2agree_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa07_04_t1 ~~ sa07_04_t1sa07_05_t1 ~~ sa07_05_t1sa07_06_t1 ~~ sa07_06_t1sa07_04_t1 ~ 1sa07_05_t1 ~ 1sa07_06_t1 ~ 1'fit_mi_lcs_agree_ideal_specif_hyp5 <-lavaan(mi_lcs_agree_ideal_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_agree_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific acceptance goal):
Correlation of specific, facet-level acceptance goals with agreeableness change score (ideal-self) is not significantly different from zero, r = 0.035, p = 0.701.
6.5.1.9 Conscientiousness - current-self: general acceptance goals
Fit model:
Show the code
# adding correlation with manifest acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_consc_curr_hyp5 <-'consc_t1 =~ 1*consc_curr_par1_t1 + lamb2*consc_curr_par2_t1 + lamb3*consc_curr_par3_t1 # This specifies the measurement model for consc_t1 consc_t2 =~ 1*consc_curr_par1_t2 + lamb2*consc_curr_par2_t2 + lamb3*consc_curr_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadingsconsc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0d_consc_1 ~ 1 # This estimates the intercept of the change score consc_t1 ~ 1 # This estimates the intercept of consc_t1 d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1 d_consc_1 ~ consc_t1 # This estimates the self-feedback parameterd_consc_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variableconsc_curr_par1_t1 ~~ consc_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2consc_curr_par2_t1 ~~ consc_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2consc_curr_par3_t1 ~~ consc_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2consc_curr_par1_t1 ~~ res1*consc_curr_par1_t1 # This allows residual variance on indicator X1 at T1 consc_curr_par2_t1 ~~ res2*consc_curr_par2_t1 # This allows residual variance on indicator X2 at T1consc_curr_par3_t1 ~~ res3*consc_curr_par3_t1 # This allows residual variance on indicator X3 at T1consc_curr_par1_t2 ~~ res1*consc_curr_par1_t2 # This allows residual variance on indicator X1 at T2 consc_curr_par2_t2 ~~ res2*consc_curr_par2_t2 # This allows residual variance on indicator X2 at T2 consc_curr_par3_t2 ~~ res3*consc_curr_par3_t2 # This allows residual variance on indicator X3 at T2consc_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1consc_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1consc_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1consc_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2consc_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2consc_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa06_01_t1 ~~ sa06_01_t1sa06_01_t1 ~ 1'fit_mi_lcs_consc_curr_hyp5 <-lavaan(mi_lcs_consc_curr_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_consc_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of general acceptance goal with conscientiousness change score (current-self) is not significantly different from zero, r = 0.062, p = 0.305.
6.5.1.10 Conscientiousness - ideal-self: general acceptance goals
Fit model:
Show the code
# adding correlation with manifest acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_consc_ideal_hyp5 <-'consc_t1 =~ 1*consc_ideal_par1_t1 + lamb2*consc_ideal_par2_t1 + lamb3*consc_ideal_par3_t1 # This specifies the measurement model for consc_t1consc_t2 =~ 1*consc_ideal_par1_t2 + lamb2*consc_ideal_par2_t2 + lamb3*consc_ideal_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadingsconsc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0d_consc_1 ~ 1 # This estimates the intercept of the change score consc_t1 ~ 1 # This estimates the intercept of consc_t1 d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1 d_consc_1 ~ consc_t1 # This estimates the self-feedback parameterd_consc_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variableconsc_ideal_par1_t1 ~~ consc_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2consc_ideal_par2_t1 ~~ consc_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2consc_ideal_par3_t1 ~~ consc_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2consc_ideal_par1_t1 ~~ res1*consc_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 consc_ideal_par2_t1 ~~ res2*consc_ideal_par2_t1 # This allows residual variance on indicator X2 at T1consc_ideal_par3_t1 ~~ res3*consc_ideal_par3_t1 # This allows residual variance on indicator X3 at T1consc_ideal_par1_t2 ~~ res1*consc_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 consc_ideal_par2_t2 ~~ res2*consc_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 consc_ideal_par3_t2 ~~ res3*consc_ideal_par3_t2 # This allows residual variance on indicator X3 at T2consc_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1consc_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1consc_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1consc_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2consc_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2consc_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa06_01_t1 ~~ sa06_01_t1sa06_01_t1 ~ 1'fit_mi_lcs_consc_ideal_hyp5 <-lavaan(mi_lcs_consc_ideal_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_consc_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of general acceptance goal with conscientiousness change score (ideal-self) is not significantly different from zero, r = -0.022, p = 0.731.
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_consc_curr_specif_hyp5 <-'consc_t1 =~ 1*consc_curr_par1_t1 + lamb2*consc_curr_par2_t1 + lamb3*consc_curr_par3_t1 # This specifies the measurement model for consc_t1 consc_t2 =~ 1*consc_curr_par1_t2 + lamb2*consc_curr_par2_t2 + lamb3*consc_curr_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadingsgoals =~ 1*sa07_07_t1 + sa07_08_t1 + sa07_09_t1 # latent acceptance goal variable (three facets per trait)consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0d_consc_1 ~ 1 # This estimates the intercept of the change score consc_t1 ~ 1 # This estimates the intercept of consc_t1 d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1 d_consc_1 ~ consc_t1 # This estimates the self-feedback parameterd_consc_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0goals ~~ goals # This estimates the variance of the (latent) acceptance goal variableconsc_curr_par1_t1 ~~ consc_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2consc_curr_par2_t1 ~~ consc_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2consc_curr_par3_t1 ~~ consc_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2consc_curr_par1_t1 ~~ res1*consc_curr_par1_t1 # This allows residual variance on indicator X1 at T1 consc_curr_par2_t1 ~~ res2*consc_curr_par2_t1 # This allows residual variance on indicator X2 at T1consc_curr_par3_t1 ~~ res3*consc_curr_par3_t1 # This allows residual variance on indicator X3 at T1consc_curr_par1_t2 ~~ res1*consc_curr_par1_t2 # This allows residual variance on indicator X1 at T2 consc_curr_par2_t2 ~~ res2*consc_curr_par2_t2 # This allows residual variance on indicator X2 at T2 consc_curr_par3_t2 ~~ res3*consc_curr_par3_t2 # This allows residual variance on indicator X3 at T2consc_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1consc_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1consc_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1consc_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2consc_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2consc_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa07_07_t1 ~~ sa07_07_t1sa07_08_t1 ~~ sa07_08_t1sa07_09_t1 ~~ sa07_09_t1sa07_07_t1 ~ 1sa07_08_t1 ~ 1sa07_09_t1 ~ 1'fit_mi_lcs_consc_curr_specif_hyp5 <-lavaan(mi_lcs_consc_curr_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_consc_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific acceptance goal):
The correlation of specific, facet-level acceptance goals with the conscientiousness change score (current-self) is significantly different from zero, r = -0.277, p = 0.009.
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_consc_ideal_specif_hyp5 <-'consc_t1 =~ 1*consc_ideal_par1_t1 + lamb2*consc_ideal_par2_t1 + lamb3*consc_ideal_par3_t1 # This specifies the measurement model for consc_t1consc_t2 =~ 1*consc_ideal_par1_t2 + lamb2*consc_ideal_par2_t2 + lamb3*consc_ideal_par3_t2 # This specifies the measurement model for consc_t2 with the equality constrained factor loadingsgoals =~ 1*sa07_07_t1 + sa07_08_t1 + sa07_09_t1 # latent acceptance goal variable (three facets per trait)consc_t2 ~ 1*consc_t1 # This parameter regresses consc_t2 perfectly on consc_t1d_consc_1 =~ 1*consc_t2 # This defines the latent change score factor as measured perfectly by scores on consc_t2consc_t2 ~ 0*1 # This line constrains the intercept of consc_t2 to 0consc_t2 ~~ 0*consc_t2 # This fixes the variance of consc_t2 to 0d_consc_1 ~ 1 # This estimates the intercept of the change score consc_t1 ~ 1 # This estimates the intercept of consc_t1 d_consc_1 ~~ d_consc_1 # This estimates the variance of the change scores consc_t1 ~~ consc_t1 # This estimates the variance of the consc_t1 d_consc_1 ~ consc_t1 # This estimates the self-feedback parameterd_consc_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0goals ~~ goals # This estimates the variance of the (latent) acceptance goal variableconsc_ideal_par1_t1 ~~ consc_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2consc_ideal_par2_t1 ~~ consc_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2consc_ideal_par3_t1 ~~ consc_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2consc_ideal_par1_t1 ~~ res1*consc_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 consc_ideal_par2_t1 ~~ res2*consc_ideal_par2_t1 # This allows residual variance on indicator X2 at T1consc_ideal_par3_t1 ~~ res3*consc_ideal_par3_t1 # This allows residual variance on indicator X3 at T1consc_ideal_par1_t2 ~~ res1*consc_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 consc_ideal_par2_t2 ~~ res2*consc_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 consc_ideal_par3_t2 ~~ res3*consc_ideal_par3_t2 # This allows residual variance on indicator X3 at T2consc_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1consc_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1consc_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1consc_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2consc_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2consc_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa07_07_t1 ~~ sa07_07_t1sa07_08_t1 ~~ sa07_08_t1sa07_09_t1 ~~ sa07_09_t1sa07_07_t1 ~ 1sa07_08_t1 ~ 1sa07_09_t1 ~ 1'fit_mi_lcs_consc_ideal_specif_hyp5 <-lavaan(mi_lcs_consc_ideal_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_consc_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific acceptance goal):
The correlation of specific, facet-level acceptance goals with the conscientiousness change score (ideal-self) is not significantly different from zero, r = -0.091, p = 0.19.
6.5.1.13 Neuroticism - current-self: general acceptance goals
Fit model:
Show the code
# adding correlation with manifest acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_neuro_curr_hyp5 <-'neuro_t1 =~ 1*neuro_curr_par1_t1 + lamb2*neuro_curr_par2_t1 + lamb3*neuro_curr_par3_t1 # This specifies the measurement model for neuro_t1 neuro_t2 =~ 1*neuro_curr_par1_t2 + lamb2*neuro_curr_par2_t2 + lamb3*neuro_curr_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadingsneuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0d_neuro_1 ~ 1 # This estimates the intercept of the change score neuro_t1 ~ 1 # This estimates the intercept of neuro_t1 d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1 d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameterd_neuro_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variableneuro_curr_par1_t1 ~~ neuro_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2neuro_curr_par2_t1 ~~ neuro_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2neuro_curr_par3_t1 ~~ neuro_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2neuro_curr_par1_t1 ~~ res1*neuro_curr_par1_t1 # This allows residual variance on indicator X1 at T1 neuro_curr_par2_t1 ~~ res2*neuro_curr_par2_t1 # This allows residual variance on indicator X2 at T1neuro_curr_par3_t1 ~~ res3*neuro_curr_par3_t1 # This allows residual variance on indicator X3 at T1neuro_curr_par1_t2 ~~ res1*neuro_curr_par1_t2 # This allows residual variance on indicator X1 at T2 neuro_curr_par2_t2 ~~ res2*neuro_curr_par2_t2 # This allows residual variance on indicator X2 at T2 neuro_curr_par3_t2 ~~ res3*neuro_curr_par3_t2 # This allows residual variance on indicator X3 at T2neuro_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1neuro_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1neuro_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1neuro_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2neuro_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2neuro_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa06_01_t1 ~~ sa06_01_t1sa06_01_t1 ~ 1'fit_mi_lcs_neuro_curr_hyp5 <-lavaan(mi_lcs_neuro_curr_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_neuro_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of general acceptance goal with neuroticism change score (current-self) is significantly different from zero, r = -0.161, p = 0.021.
6.5.1.14 Neuroticism - ideal-self: general acceptance goals
Fit model:
Show the code
# adding correlation with manifest acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_neuro_ideal_hyp5 <-'neuro_t1 =~ 1*neuro_ideal_par1_t1 + lamb2*neuro_ideal_par2_t1 + lamb3*neuro_ideal_par3_t1 # This specifies the measurement model for neuro_t1 neuro_t2 =~ 1*neuro_ideal_par1_t2 + lamb2*neuro_ideal_par2_t2 + lamb3*neuro_ideal_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadingsneuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0d_neuro_1 ~ 1 # This estimates the intercept of the change score neuro_t1 ~ 1 # This estimates the intercept of neuro_t1 d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1 d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameterd_neuro_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variableneuro_ideal_par1_t1 ~~ neuro_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2neuro_ideal_par1_t1 ~~ res1*neuro_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 neuro_ideal_par2_t1 ~~ res2*neuro_ideal_par2_t1 # This allows residual variance on indicator X2 at T1neuro_ideal_par3_t1 ~~ res3*neuro_ideal_par3_t1 # This allows residual variance on indicator X3 at T1neuro_ideal_par1_t2 ~~ res1*neuro_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 neuro_ideal_par2_t2 ~~ res2*neuro_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 neuro_ideal_par3_t2 ~~ res3*neuro_ideal_par3_t2 # This allows residual variance on indicator X3 at T2neuro_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1neuro_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1neuro_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1neuro_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2neuro_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2neuro_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa06_01_t1 ~~ sa06_01_t1sa06_01_t1 ~ 1'fit_mi_lcs_neuro_ideal_hyp5 <-lavaan(mi_lcs_neuro_ideal_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_neuro_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sa06_01_t1 = general acceptance goal):
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_neuro_curr_specif_hyp5 <-'neuro_t1 =~ 1*neuro_curr_par1_t1 + lamb2*neuro_curr_par2_t1 + lamb3*neuro_curr_par3_t1 # This specifies the measurement model for neuro_t1 neuro_t2 =~ 1*neuro_curr_par1_t2 + lamb2*neuro_curr_par2_t2 + lamb3*neuro_curr_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadingsgoals =~ 1*sa07_10_t1 + sa07_11_t1 + sa07_12_t1 # latent acceptance goal variable (three facets per trait)neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0d_neuro_1 ~ 1 # This estimates the intercept of the change score neuro_t1 ~ 1 # This estimates the intercept of neuro_t1 d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1 d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameterd_neuro_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0goals ~~ goals # This estimates the variance of the (latent) acceptance goal variableneuro_curr_par1_t1 ~~ neuro_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2neuro_curr_par2_t1 ~~ neuro_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2neuro_curr_par3_t1 ~~ neuro_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2neuro_curr_par1_t1 ~~ res1*neuro_curr_par1_t1 # This allows residual variance on indicator X1 at T1 neuro_curr_par2_t1 ~~ res2*neuro_curr_par2_t1 # This allows residual variance on indicator X2 at T1neuro_curr_par3_t1 ~~ res3*neuro_curr_par3_t1 # This allows residual variance on indicator X3 at T1neuro_curr_par1_t2 ~~ res1*neuro_curr_par1_t2 # This allows residual variance on indicator X1 at T2 neuro_curr_par2_t2 ~~ res2*neuro_curr_par2_t2 # This allows residual variance on indicator X2 at T2 neuro_curr_par3_t2 ~~ res3*neuro_curr_par3_t2 # This allows residual variance on indicator X3 at T2neuro_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1neuro_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1neuro_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1neuro_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2neuro_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2neuro_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa07_10_t1 ~~ sa07_10_t1sa07_11_t1 ~~ sa07_11_t1sa07_12_t1 ~~ sa07_12_t1sa07_10_t1 ~ 1sa07_11_t1 ~ 1sa07_12_t1 ~ 1'fit_mi_lcs_neuro_curr_specif_hyp5 <-lavaan(mi_lcs_neuro_curr_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_neuro_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific acceptance goal):
Correlation of specific, facet-level acceptance goals with neuroticism change score (current-self) is not significantly different from zero, r = 0.055, p = 0.516.
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_neuro_ideal_specif_hyp5 <-'neuro_t1 =~ 1*neuro_ideal_par1_t1 + lamb2*neuro_ideal_par2_t1 + lamb3*neuro_ideal_par3_t1 # This specifies the measurement model for neuro_t1 neuro_t2 =~ 1*neuro_ideal_par1_t2 + lamb2*neuro_ideal_par2_t2 + lamb3*neuro_ideal_par3_t2 # This specifies the measurement model for neuro_t2 with the equality constrained factor loadingsgoals =~ 1*sa07_10_t1 + sa07_11_t1 + sa07_12_t1 # latent acceptance goal variable (three facets per trait)neuro_t2 ~ 1*neuro_t1 # This parameter regresses neuro_t2 perfectly on neuro_t1d_neuro_1 =~ 1*neuro_t2 # This defines the latent change score factor as measured perfectly by scores on neuro_t2neuro_t2 ~ 0*1 # This line constrains the intercept of neuro_t2 to 0neuro_t2 ~~ 0*neuro_t2 # This fixes the variance of neuro_t2 to 0d_neuro_1 ~ 1 # This estimates the intercept of the change score neuro_t1 ~ 1 # This estimates the intercept of neuro_t1 d_neuro_1 ~~ d_neuro_1 # This estimates the variance of the change scores neuro_t1 ~~ neuro_t1 # This estimates the variance of the neuro_t1 d_neuro_1 ~ neuro_t1 # This estimates the self-feedback parameterd_neuro_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0goals ~~ goals # This estimates the variance of the (latent) acceptance goal variableneuro_ideal_par1_t1 ~~ neuro_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2neuro_ideal_par2_t1 ~~ neuro_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2neuro_ideal_par3_t1 ~~ neuro_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2neuro_ideal_par1_t1 ~~ res1*neuro_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 neuro_ideal_par2_t1 ~~ res2*neuro_ideal_par2_t1 # This allows residual variance on indicator X2 at T1neuro_ideal_par3_t1 ~~ res3*neuro_ideal_par3_t1 # This allows residual variance on indicator X3 at T1neuro_ideal_par1_t2 ~~ res1*neuro_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 neuro_ideal_par2_t2 ~~ res2*neuro_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 neuro_ideal_par3_t2 ~~ res3*neuro_ideal_par3_t2 # This allows residual variance on indicator X3 at T2neuro_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1neuro_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1neuro_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1neuro_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2neuro_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2neuro_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa07_10_t1 ~~ sa07_10_t1sa07_11_t1 ~~ sa07_11_t1sa07_12_t1 ~~ sa07_12_t1sa07_10_t1 ~ 1sa07_11_t1 ~ 1sa07_12_t1 ~ 1'fit_mi_lcs_neuro_ideal_specif_hyp5 <-lavaan(mi_lcs_neuro_ideal_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_neuro_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific acceptance goal):
The correlation of specific, facet-level acceptance goals with the neuroticism change score (ideal-self) is significantly different from zero, r = 0.148, p = 0.02.
6.5.1.17 Openness - current-self: general acceptance goals
Fit model:
Show the code
# adding correlation with manifest acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_openn_curr_hyp5 <-'openn_t1 =~ 1*openn_curr_par1_t1 + lamb2*openn_curr_par2_t1 + lamb3*openn_curr_par3_t1 # This specifies the measurement model for openn_t1openn_t2 =~ 1*openn_curr_par1_t2 + lamb2*openn_curr_par2_t2 + lamb3*openn_curr_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadingsopenn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0d_openn_1 ~ 1 # This estimates the intercept of the change score openn_t1 ~ 1 # This estimates the intercept of openn_t1 d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1 d_openn_1 ~ openn_t1 # This estimates the self-feedback parameterd_openn_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variableopenn_curr_par1_t1 ~~ openn_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2openn_curr_par2_t1 ~~ openn_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2openn_curr_par3_t1 ~~ openn_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2openn_curr_par1_t1 ~~ res1*openn_curr_par1_t1 # This allows residual variance on indicator X1 at T1 openn_curr_par2_t1 ~~ res2*openn_curr_par2_t1 # This allows residual variance on indicator X2 at T1openn_curr_par3_t1 ~~ res3*openn_curr_par3_t1 # This allows residual variance on indicator X3 at T1openn_curr_par1_t2 ~~ res1*openn_curr_par1_t2 # This allows residual variance on indicator X1 at T2 openn_curr_par2_t2 ~~ res2*openn_curr_par2_t2 # This allows residual variance on indicator X2 at T2 openn_curr_par3_t2 ~~ res3*openn_curr_par3_t2 # This allows residual variance on indicator X3 at T2openn_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1openn_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1openn_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1openn_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2openn_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2openn_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa06_01_t1 ~~ sa06_01_t1sa06_01_t1 ~ 1'fit_mi_lcs_openn_curr_hyp5 <-lavaan(mi_lcs_openn_curr_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_openn_curr_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of general acceptance goal with openness change score (current-self) is not significantly different from zero, r = 0.097, p = 0.176.
6.5.1.18 Openness - ideal-self: general acceptance goals
Fit model:
Show the code
# adding correlation with manifest acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_openn_ideal_hyp5 <-'openn_t1 =~ 1*openn_ideal_par1_t1 + lamb2*openn_ideal_par2_t1 + lamb3*openn_ideal_par3_t1 # This specifies the measurement model for openn_t1 openn_t2 =~ 1*openn_ideal_par1_t2 + lamb2*openn_ideal_par2_t2 + lamb3*openn_ideal_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadingsopenn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0d_openn_1 ~ 1 # This estimates the intercept of the change score openn_t1 ~ 1 # This estimates the intercept of openn_t1 d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1 d_openn_1 ~ openn_t1 # This estimates the self-feedback parameterd_openn_1 ~~ sa06_01_t1 # estimates the covariance/correlation with acceptance goal variableopenn_ideal_par1_t1 ~~ openn_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2openn_ideal_par2_t1 ~~ openn_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2openn_ideal_par3_t1 ~~ openn_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2openn_ideal_par1_t1 ~~ res1*openn_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 openn_ideal_par2_t1 ~~ res2*openn_ideal_par2_t1 # This allows residual variance on indicator X2 at T1openn_ideal_par3_t1 ~~ res3*openn_ideal_par3_t1 # This allows residual variance on indicator X3 at T1openn_ideal_par1_t2 ~~ res1*openn_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 openn_ideal_par2_t2 ~~ res2*openn_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 openn_ideal_par3_t2 ~~ res3*openn_ideal_par3_t2 # This allows residual variance on indicator X3 at T2openn_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1openn_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1openn_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1openn_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2openn_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2openn_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa06_01_t1 ~~ sa06_01_t1sa06_01_t1 ~ 1'fit_mi_lcs_openn_ideal_hyp5 <-lavaan(mi_lcs_openn_ideal_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing="fiml")summary(fit_mi_lcs_openn_ideal_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (sa06_01_t1 = general acceptance goal):
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_openn_curr_specif_hyp5 <-'openn_t1 =~ 1*openn_curr_par1_t1 + lamb2*openn_curr_par2_t1 + lamb3*openn_curr_par3_t1 # This specifies the measurement model for openn_t1openn_t2 =~ 1*openn_curr_par1_t2 + lamb2*openn_curr_par2_t2 + lamb3*openn_curr_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadingsgoals =~ 1*sa07_13_t1 + sa07_14_t1 + sa07_15_t1 # latent acceptance goal variable (three facets per trait)openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0d_openn_1 ~ 1 # This estimates the intercept of the change score openn_t1 ~ 1 # This estimates the intercept of openn_t1 d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1 d_openn_1 ~ openn_t1 # This estimates the self-feedback parameterd_openn_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0goals ~~ goals # This estimates the variance of the (latent) acceptance goal variableopenn_curr_par1_t1 ~~ openn_curr_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2openn_curr_par2_t1 ~~ openn_curr_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2openn_curr_par3_t1 ~~ openn_curr_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2openn_curr_par1_t1 ~~ res1*openn_curr_par1_t1 # This allows residual variance on indicator X1 at T1 openn_curr_par2_t1 ~~ res2*openn_curr_par2_t1 # This allows residual variance on indicator X2 at T1openn_curr_par3_t1 ~~ res3*openn_curr_par3_t1 # This allows residual variance on indicator X3 at T1openn_curr_par1_t2 ~~ res1*openn_curr_par1_t2 # This allows residual variance on indicator X1 at T2 openn_curr_par2_t2 ~~ res2*openn_curr_par2_t2 # This allows residual variance on indicator X2 at T2 openn_curr_par3_t2 ~~ res3*openn_curr_par3_t2 # This allows residual variance on indicator X3 at T2openn_curr_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1openn_curr_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1openn_curr_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1openn_curr_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2openn_curr_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2openn_curr_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa07_13_t1 ~~ sa07_13_t1sa07_14_t1 ~~ sa07_14_t1sa07_15_t1 ~~ sa07_15_t1sa07_13_t1 ~ 1sa07_14_t1 ~ 1sa07_15_t1 ~ 1'fit_mi_lcs_openn_curr_specif_hyp5 <-lavaan(mi_lcs_openn_curr_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')summary(fit_mi_lcs_openn_curr_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific acceptance goal):
Correlation of specific, facet-level acceptance goals with openness change score (current-self) is not significantly different from zero, r = -0.064, p = 0.455.
# adding correlation with latent (made up of the three facets) acceptance goal variable to the latent change score model:# Fit the multiple indicator univariate latent change score modelmi_lcs_openn_ideal_specif_hyp5 <-'openn_t1 =~ 1*openn_ideal_par1_t1 + lamb2*openn_ideal_par2_t1 + lamb3*openn_ideal_par3_t1 # This specifies the measurement model for openn_t1 openn_t2 =~ 1*openn_ideal_par1_t2 + lamb2*openn_ideal_par2_t2 + lamb3*openn_ideal_par3_t2 # This specifies the measurement model for openn_t2 with the equality constrained factor loadingsgoals =~ 1*sa07_13_t1 + sa07_14_t1 + sa07_15_t1 # latent acceptance goal variable (three facets per trait)openn_t2 ~ 1*openn_t1 # This parameter regresses openn_t2 perfectly on openn_t1d_openn_1 =~ 1*openn_t2 # This defines the latent change score factor as measured perfectly by scores on openn_t2openn_t2 ~ 0*1 # This line constrains the intercept of openn_t2 to 0openn_t2 ~~ 0*openn_t2 # This fixes the variance of openn_t2 to 0d_openn_1 ~ 1 # This estimates the intercept of the change score openn_t1 ~ 1 # This estimates the intercept of openn_t1 d_openn_1 ~~ d_openn_1 # This estimates the variance of the change scores openn_t1 ~~ openn_t1 # This estimates the variance of the openn_t1 d_openn_1 ~ openn_t1 # This estimates the self-feedback parameterd_openn_1 ~~ goals # estimates the covariance/correlation with the (latent) acceptance goal variablegoals ~ 0*1 # This fixes the intercept of the (latent) acceptance goal variable to 0goals ~~ goals # This estimates the variance of the (latent) acceptance goal variableopenn_ideal_par1_t1 ~~ openn_ideal_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2openn_ideal_par2_t1 ~~ openn_ideal_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2openn_ideal_par3_t1 ~~ openn_ideal_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2openn_ideal_par1_t1 ~~ res1*openn_ideal_par1_t1 # This allows residual variance on indicator X1 at T1 openn_ideal_par2_t1 ~~ res2*openn_ideal_par2_t1 # This allows residual variance on indicator X2 at T1openn_ideal_par3_t1 ~~ res3*openn_ideal_par3_t1 # This allows residual variance on indicator X3 at T1openn_ideal_par1_t2 ~~ res1*openn_ideal_par1_t2 # This allows residual variance on indicator X1 at T2 openn_ideal_par2_t2 ~~ res2*openn_ideal_par2_t2 # This allows residual variance on indicator X2 at T2 openn_ideal_par3_t2 ~~ res3*openn_ideal_par3_t2 # This allows residual variance on indicator X3 at T2openn_ideal_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1openn_ideal_par2_t1 ~ m2*1 # This estimates the intercept of X2 at T1openn_ideal_par3_t1 ~ m3*1 # This estimates the intercept of X3 at T1openn_ideal_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2openn_ideal_par2_t2 ~ m2*1 # This estimates the intercept of X2 at T2openn_ideal_par3_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa07_13_t1 ~~ sa07_13_t1sa07_14_t1 ~~ sa07_14_t1sa07_15_t1 ~~ sa07_15_t1sa07_13_t1 ~ 1sa07_14_t1 ~ 1sa07_15_t1 ~ 1'fit_mi_lcs_openn_ideal_specif_hyp5 <-lavaan(mi_lcs_openn_ideal_specif_hyp5, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing ="fiml")summary(fit_mi_lcs_openn_ideal_specif_hyp5, fit.measures=TRUE, standardized=TRUE, rsquare=F)
Results summary (goals = trait/facet specific acceptance goal):
The correlation of specific, facet-level acceptance goals with the openness change score (ideal-self) is not significantly different from zero, r = -0.017, p = 0.847.
6.5.2 Big Five facets
Run models for all facets with a template & loop:
Show the code
# create template:facet_template <-'facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0d_facet_1 ~ 1 # This estimates the intercept of the change score facet_t1 ~ 1 # This estimates the intercept of facet_t1 d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1 d_facet_1 ~ facet_t1 # This estimates the self-feedback parameterd_facet_1 ~~ ind_goal # estimates the covariance/correlation with acceptance goal variableind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1 ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2 ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2 ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2ind_goal ~~ ind_goalind_goal ~ 1'# loop across 15 facetsfor (i in6:length(b5_vars)) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# loop across 2 BFI versions (combined pre&post current/ideal)for (j in5:length(bfi_versions)) { items =paste0(bfi_versions[[j]], item_nrs)# loop across 2 different goal operationalizations (sa06_01_t1 & sa07_XX_t1)for (k in1:2) {if (k==1) { goal_op ="sa06_01_t1" } else{ goal_op =paste0("sa07_", str_pad(i-5, 2, pad ="0"), "_t1") } template_filled <-str_replace_all(facet_template, c("facet"= short_name,"ind1"= items[1], "ind2"= items[2], "ind3"= items[3], "ind4"= items[4],"ind_goal"= goal_op)) facet_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sa, estimator='mlr', fixed.x=FALSE, missing='fiml')# save to environmentif (k==1) {eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[j], 6), "_hyp5")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[j], 6), "_hyp5")), facet_model_fit)) } else{eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[j], 6), "_specif_hyp5")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[j], 6), "_specif_hyp5")), facet_model_fit)) } } }}
6.5.2.1 Sociability - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
The correlation of the general acceptance goal with the sociability change score (current-self) is significantly different from zero, r = 0.268, p = 0.001.
6.5.2.2 Sociability - ideal-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with sociability change score (current-self) is not significantly different from zero, r = 0.004, p = 0.969.
Correlation of specific, facet-level acceptance goals with anxiety change score (ideal-self) is not significantly different from zero, r = -0.027, p = 0.793.
6.5.2.5 Assertiveness - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with assertiveness change score (current-self) is not significantly different from zero, r = -0.022, p = 0.8.
Correlation of specific, facet-level acceptance goals with assertiveness change score (ideal-self) is not significantly different from zero, r = -0.084, p = 0.372.
6.5.2.9 Energy - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with energy change score (current-self) is not significantly different from zero, r = 0.129, p = 0.17.
6.5.2.12 Energy - ideal-self: specific, facet-level acceptance goals
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
Correlation of specific, facet-level acceptance goals with energy change score (ideal-self) is not significantly different from zero, r = 0.147, p = 0.156.
6.5.2.13 Compassion - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with compassion change score (current-self) is not significantly different from zero, r = -0.046, p = 0.566.
Correlation of specific, facet-level acceptance goals with compassion change score (ideal-self) is not significantly different from zero, r = -0.214, p = 0.047.
6.5.2.17 Respectfulness - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with respectfulness change score (current-self) is not significantly different from zero, r = 0.038, p = 0.698.
Correlation of specific, facet-level acceptance goals with respectfulness change score (ideal-self) is not significantly different from zero, r = -0.031, p = 0.753.
6.5.2.21 Trust - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with trust change score (current-self) is not significantly different from zero, r = 0.109, p = 0.225.
Correlation of specific, facet-level acceptance goals with trust change score (ideal-self) is not significantly different from zero, r = 0.026, p = 0.779.
6.5.2.25 Organization - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
The correlation of specific, facet-level acceptance goals with the organization change score (current-self) is significantly different from zero, r = 0.236, p = 0.017.
Correlation of specific, facet-level acceptance goals with organization change score (ideal-self) is not significantly different from zero, r = 0.055, p = 0.481.
6.5.2.29 Productiveness - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of general acceptance goal with productiveness change score (current-self) is not significantly different from zero, r = -0.068, p = 0.386.
6.5.2.30 Productiveness - ideal-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with productiveness change score (current-self) is not significantly different from zero, r = 0.19, p = 0.072.
Correlation of specific, facet-level acceptance goals with productiveness change score (ideal-self) is not significantly different from zero, r = 0.024, p = 0.779.
6.5.2.33 Responsibility - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
The correlation of specific, facet-level acceptance goals with the responsibility change score (current-self) is significantly different from zero, r = -0.342, p = 0.006.
Correlation of specific, facet-level acceptance goals with responsibility change score (ideal-self) is not significantly different from zero, r = -0.089, p = 0.295.
6.5.2.37 Anxiety - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with anxiety change score (current-self) is not significantly different from zero, r = 0.016, p = 0.866.
Correlation of specific, facet-level acceptance goals with anxiety change score (ideal-self) is not significantly different from zero, r = 0.038, p = 0.76.
6.5.2.41 Depression - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with depression change score (current-self) is not significantly different from zero, r = -0.083, p = 0.377.
Correlation of specific, facet-level acceptance goals with depression change score (ideal-self) is not significantly different from zero, r = -0.001, p = 0.992.
6.5.2.45 Volatility - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with volatility change score (current-self) is not significantly different from zero, r = -0.007, p = 0.932.
Correlation of specific, facet-level acceptance goals with volatility change score (ideal-self) is not significantly different from zero, r = -0.074, p = 0.415.
6.5.2.49 Curiosity - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
The correlation of specific, facet-level acceptance goals with the curiosity change score (current-self) is significantly different from zero, r = -0.187, p = 0.075.
Correlation of specific, facet-level acceptance goals with curiosity change score (ideal-self) is not significantly different from zero, r = -0.184, p = 0.105.
6.5.2.53 Aesthetic - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with aesthetic change score (current-self) is not significantly different from zero, r = -0.015, p = 0.851.
Correlation of specific, facet-level acceptance goals with aesthetic change score (ideal-self) is not significantly different from zero, r = -0.02, p = 0.786.
6.5.2.57 Imagination - current-self: general acceptance goals
Results summary (sa06_01_t1 = general acceptance goal):
Correlation of specific, facet-level acceptance goals with imagination change score (current-self) is not significantly different from zero, r = -0.092, p = 0.317.
Correlation of specific, facet-level acceptance goals with imagination change score (ideal-self) is not significantly different from zero, r = 0.027, p = 0.793.
Results summary across the Big Five traits: covariance of the latent change score and acceptance goal(s)
kable(df_table_hyp5[1:20, ], digits =3)
trait
ref
goal
estimate
std.all
statistic
p.value
extraversion
current
general
0.062
0.193
3.212
0.001
extraversion
ideal
general
0.024
0.076
1.114
0.265
extraversion
current
specific
0.017
0.052
0.496
0.620
extraversion
ideal
specific
-0.031
-0.097
-0.905
0.365
agreeableness
current
general
0.027
0.145
1.520
0.129
agreeableness
ideal
general
0.024
0.084
1.219
0.223
agreeableness
current
specific
0.005
0.035
0.322
0.748
agreeableness
ideal
specific
0.008
0.035
0.383
0.701
conscientiousness
current
general
0.020
0.062
1.025
0.305
conscientiousness
ideal
general
-0.006
-0.022
-0.344
0.731
conscientiousness
current
specific
-0.108
-0.277
-2.610
0.009
conscientiousness
ideal
specific
-0.032
-0.091
-1.310
0.190
neuroticism
current
general
-0.063
-0.161
-2.306
0.021
neuroticism
ideal
general
-0.013
-0.046
-0.728
0.467
neuroticism
current
specific
0.020
0.055
0.650
0.516
neuroticism
ideal
specific
0.036
0.148
2.319
0.020
openness
current
general
0.025
0.097
1.354
0.176
openness
ideal
general
0.001
0.007
0.088
0.930
openness
current
specific
-0.014
-0.064
-0.747
0.455
openness
ideal
specific
-0.003
-0.017
-0.193
0.847
Five covariances significantly differ from zero:
changes in current-level extraversion covary with the general acceptance goal
changes in current-level conscientiousness covary with the specific acceptance goals (latent factor of the three C facets) -> unexpected direction of the effect!
changes in ideal-level neuroticism covary with the specific acceptance goals (latent factor of the three N facets)
changes in ideal-level openness covary with the general acceptance goal
Results summary across the Big Five facets: covariance of the latent change score and acceptance goal(s)
kable(df_table_hyp5[21:80, ], digits =3)
trait
ref
goal
estimate
std.all
statistic
p.value
sociability
current
general
0.089
0.268
3.243
0.001
sociability
ideal
general
-0.013
-0.060
-0.767
0.443
sociability
current
specific
0.002
0.004
0.039
0.969
sociability
ideal
specific
-0.009
-0.027
-0.262
0.793
assertiveness
current
general
0.056
0.181
2.425
0.015
assertiveness
ideal
general
0.015
0.089
1.023
0.306
assertiveness
current
specific
-0.010
-0.022
-0.253
0.800
assertiveness
ideal
specific
-0.021
-0.084
-0.893
0.372
energy
current
general
-0.019
-0.068
-0.985
0.325
energy
ideal
general
-0.028
-0.147
-1.512
0.130
energy
current
specific
0.054
0.129
1.373
0.170
energy
ideal
specific
0.042
0.147
1.420
0.156
compassion
current
general
0.060
0.142
1.452
0.147
compassion
ideal
general
0.012
0.041
0.402
0.688
compassion
current
specific
-0.027
-0.046
-0.574
0.566
compassion
ideal
specific
-0.088
-0.214
-1.988
0.047
respectfulness
current
general
0.012
0.049
0.539
0.590
respectfulness
ideal
general
0.011
0.054
0.624
0.533
respectfulness
current
specific
0.013
0.038
0.388
0.698
respectfulness
ideal
specific
-0.009
-0.031
-0.315
0.753
trust
current
general
-0.019
-0.067
-0.717
0.473
trust
ideal
general
-0.045
-0.174
-1.867
0.062
trust
current
specific
0.045
0.109
1.213
0.225
trust
ideal
specific
0.010
0.026
0.281
0.779
organization
current
general
-0.004
-0.010
-0.133
0.894
organization
ideal
general
0.007
0.041
0.528
0.598
organization
current
specific
0.164
0.236
2.380
0.017
organization
ideal
specific
0.015
0.055
0.704
0.481
productiveness
current
general
-0.023
-0.068
-0.868
0.386
productiveness
ideal
general
-0.012
-0.051
-0.619
0.536
productiveness
current
specific
0.099
0.190
1.798
0.072
productiveness
ideal
specific
0.008
0.024
0.281
0.779
responsibility
current
general
0.035
0.149
1.459
0.145
responsibility
ideal
general
0.013
0.042
0.488
0.625
responsibility
current
specific
-0.132
-0.342
-2.768
0.006
responsibility
ideal
specific
-0.044
-0.089
-1.047
0.295
anxiety
current
general
0.060
0.136
1.468
0.142
anxiety
ideal
general
0.018
0.082
0.875
0.382
anxiety
current
specific
0.011
0.016
0.169
0.866
anxiety
ideal
specific
0.014
0.038
0.305
0.760
depression
current
general
0.028
0.076
1.152
0.249
depression
ideal
general
-0.006
-0.032
-0.408
0.684
depression
current
specific
-0.049
-0.083
-0.884
0.377
depression
ideal
specific
0.000
-0.001
-0.010
0.992
volatility
current
general
-0.071
-0.148
-1.923
0.054
volatility
ideal
general
-0.056
-0.194
-2.338
0.019
volatility
current
specific
-0.005
-0.007
-0.085
0.932
volatility
ideal
specific
-0.030
-0.074
-0.815
0.415
curiosity
current
general
0.033
0.114
1.197
0.231
curiosity
ideal
general
0.016
0.092
0.961
0.336
curiosity
current
specific
-0.081
-0.187
-1.783
0.075
curiosity
ideal
specific
-0.047
-0.184
-1.621
0.105
aesthetic
current
general
-0.002
-0.084
-1.202
0.229
aesthetic
ideal
general
0.001
0.034
0.440
0.660
aesthetic
current
specific
0.000
-0.015
-0.188
0.851
aesthetic
ideal
specific
-0.001
-0.020
-0.271
0.786
imagination
current
general
0.029
0.089
1.013
0.311
imagination
ideal
general
-0.015
-0.076
-0.732
0.464
imagination
current
specific
-0.046
-0.092
-1.001
0.317
imagination
ideal
specific
0.008
0.027
0.262
0.793
Looking at the facets, we find four covariances that significantly differ from zero (relatively unsystematic across facets / current-ideal / goal dimension):
- Changes in current-level sociability covary with the general acceptance goal.
- Further, changes in current-level organization and responsibility covary with the respective specific facet acceptance goal.
- Changes in ideal-level volatility covary with the general acceptance goal
6.6 H6: Desire to change and frequency of self-improvement behaviors as moderators of change in personality in self-improvement group (H2 in paper)
Desire to change and frequency of self-improvement behaviors measured at the follow-up assessment will be positively related to change in current-self ratings in the self-improvement group.
To test this hypothesis, we will estimate the mean-level difference in current trait ratings between baseline and follow up using a latent change model for each big five domain and facet. We will then include two moderators. The first will indicate how much the individual wanted to change on a given big five domain or facet. The second will indicate their frequency of self-improvement behaviors. We will estimate the main effects of each of these variables and the interaction between these variables on the trait change score.
# create templates:# 1st, for facet-specific change goalstrait_template_mod_goal <-'trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for trait_t1 trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadingsgoals =~ 1*ind_goal_1 + ind_goal_2 + ind_goal_3 # latent variable for moderatortrait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0d_trait_1 ~ 1 # This estimates the intercept of the change score trait_t1 ~ 1 # This estimates the intercept of trait_t1 d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1 trait_t1 ~ goals # This estimates the moderation effect on personality at T1d_trait_1 ~ trait_t1 + goals # This estimates the self-feedback parameter and the moderation effect on the change scoregoals ~ 0*1 # This fixes the intercept of the moderator to 0goals ~~ goals # This estimates the variance of the moderatorind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1 ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2 ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2 ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2ind_goal_1 ~~ ind_goal_1ind_goal_2 ~~ ind_goal_2ind_goal_3 ~~ ind_goal_3ind_goal_1 ~ 1ind_goal_2 ~ 1ind_goal_3 ~ 1'trait_facets_nrs <-list(a1 =c(1:3), b2 =c(4:6), c3 =c(7:9), d4 =c(10:12), e5 =c(13:15)) # matching facet nrs to traits # loop across 5 traitsfor (i in1:5) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post current# items = paste0(bfi_versions[[5]], item_nrs) # using parcels instead! mod_names =paste0("sb07_", str_pad(trait_facets_nrs[[i]], 2, pad ="0"), "_t1") template_filled <-str_replace_all(trait_template_mod_goal, c("trait"= short_name,"ind01"=paste0(short_name, "_curr_par1"), "ind02"=paste0(short_name, "_curr_par2"), "ind03"=paste0(short_name, "_curr_par3"), "ind_goal_1"= mod_names[1], "ind_goal_2"= mod_names[2], "ind_goal_3"= mod_names[3])) trait_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_specif_hyp6")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_specif_hyp6")), trait_model_fit))} # 2nd, for frequency of self-improvement behaviortrait_template_mod_frequ <-'trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for extra_t1 trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadingsfrequ =~ 1*sb04_01_t2 + sb04_02_t2 + sb04_03_t2 # latent variable for moderatortrait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0d_trait_1 ~ 1 # This estimates the intercept of the change score trait_t1 ~ 1 # This estimates the intercept of trait_t1 d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1 trait_t1 ~ frequ # This estimates the moderation effect on personality at T1d_trait_1 ~ trait_t1 + frequ # This estimates the self-feedback parameter and the moderation effect on the change scorefrequ ~ 0*1 # This fixes the intercept of the moderator to 0frequ ~~ frequ # This estimates the variance of the moderatorind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1 ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2 ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2 ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2sb04_01_t2 ~~ sb04_01_t2sb04_02_t2 ~~ sb04_02_t2sb04_03_t2 ~~ sb04_03_t2sb04_01_t2 ~ 1sb04_02_t2 ~ 1sb04_03_t2 ~ 1'# loop across 5 traitsfor (i in1:5) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post current# items = paste0(bfi_versions[[5]], item_nrs) # using parcels instead! template_filled <-str_replace_all(trait_template_mod_frequ, c("trait"= short_name,"ind01"=paste0(short_name, "_curr_par1"), "ind02"=paste0(short_name, "_curr_par2"), "ind03"=paste0(short_name, "_curr_par3"))) trait_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6")), trait_model_fit))}
6.6.1.1 Extraversion: specific, facet-level change goals as moderator of change
Results summary (goals = trait/facet specific change goal):
The moderation effect of specific, facet-level change goals with the extraversion change score (current-self) is not significantly different from zero, b = 0.063, p = 0.324.
6.6.1.2 Extraversion: frequency of self-improvement behaviors as moderator of change
Results summary (frequ = frequency of self improvement behavior):
The moderation effect of the frequency of self-improvement behaviors with the extraversion change score (current-self) is not significantly different from zero, b = 0.076, p = 0.09.
6.6.1.3 Agreeableness: specific, facet-level change goals as moderator of change
Results summary (goals = trait/facet specific change goal):
The moderation effect of specific, facet-level change goals with the agreeableness change score (current-self) is not significantly different from zero, b = 0.022, p = 0.362.
6.6.1.4 Agreeableness: frequency of self-improvement behaviors as moderator of change
Results summary (frequ = frequency of self improvement behavior):
The moderation effect of specific, facet-level change goals with the conscientiousness change score (current-self) is not significantly different from zero, b = 0.002, p = 0.936.
6.6.1.6 Conscientiousness: frequency of self-improvement behaviors as moderator of change
Results summary (frequ = frequency of self improvement behavior):
The moderation effect of specific, facet-level change goals with the neuroticism change score (current-self) is not significantly different from zero, b = 0.085, p = 0.071.
6.6.1.8 Neuroticism: frequency of self-improvement behaviors as moderator of change
Results summary (frequ = frequency of self improvement behavior):
The moderation effect of the frequency of self-improvement behaviors with the neuroticism change score (current-self) is not significantly different from zero, b = -0.066, p = 0.2.
6.6.1.9 Openness: specific, facet-level change goals as moderator of change
Results summary (goals = trait/facet specific change goal):
The frequency of self-improvement behaviors significantly moderates changes in openness (current-self), b = 0.075, p = 0.028.
6.6.2 Big Five facets
Run models for all facets with a template & loop:
Show the code
# create templates:# 1st, for facet-specific change goalfacet_template_mod_goal <-'facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0d_facet_1 ~ 1 # This estimates the intercept of the change score facet_t1 ~ 1 # This estimates the intercept of facet_t1 d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1 facet_t1 ~ ind_goal # This estimates the moderation effect on personality at T1d_facet_1 ~ facet_t1 + ind_goal # This estimates the self-feedback parameter and the moderation effect on the change scoreind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1 ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2 ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2 ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2ind_goal ~~ ind_goalind_goal ~ 1'# loop across 15 facetsfor (i in6:length(b5_vars)) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post current items =paste0(bfi_versions[[5]], item_nrs) mod_name =paste0("sb07_", str_pad(i-5, 2, pad ="0"), "_t1") template_filled <-str_replace_all(facet_template_mod_goal, c("facet"= short_name,"ind1"= items[1], "ind2"= items[2], "ind3"= items[3], "ind4"= items[4],"ind_goal"= mod_name)) facet_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_specif_hyp6")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_specif_hyp6")), facet_model_fit))} # 2nd, for frequency of self-improvement behaviorfacet_template_mod_frequ <-'facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)frequ =~ 1*sb04_01_t2 + sb04_02_t2 + sb04_03_t2 # latent variable for moderatorfacet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0d_facet_1 ~ 1 # This estimates the intercept of the change score facet_t1 ~ 1 # This estimates the intercept of facet_t1 d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1 facet_t1 ~ frequ # This estimates the moderation effect on personality at T1d_facet_1 ~ facet_t1 + frequ # This estimates the self-feedback parameter and the moderation effect on the change scorefrequ ~ 0*1 # This fixes the intercept of the moderator to 0frequ ~~ frequ # This estimates the variance of the moderatorind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1 ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2 ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2 ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2sb04_01_t2 ~~ sb04_01_t2sb04_02_t2 ~~ sb04_02_t2sb04_03_t2 ~~ sb04_03_t2sb04_01_t2 ~ 1sb04_02_t2 ~ 1sb04_03_t2 ~ 1'# loop across 15 facetsfor (i in6:length(b5_vars)) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post current items =paste0(bfi_versions[[5]], item_nrs) template_filled <-str_replace_all(facet_template_mod_frequ, c("facet"= short_name,"ind1"= items[1], "ind2"= items[2], "ind3"= items[3], "ind4"= items[4])) facet_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sb_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_frequ_hyp6")), facet_model_fit))}
6.6.2.1 Sociability - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the sociability change score (current-self) is not significantly different from zero, b = -0.003, p = 0.92.
6.6.2.2 Sociability - frequency of self-improvement behaviors as moderator of change
Results summary (frequ = frequency of self-improvement behavior):
The moderation effect of the frequency of self-improvement behaviors with sociability change score (current-self) is not significantly different from zero, b = 0.042, p = 0.503.
6.6.2.3 Assertiveness - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the assertiveness change score (current-self) is not significantly different from zero, b = 0.03, p = 0.248.
6.6.2.4 Assertiveness - frequency of self-improvement behaviors as moderator of change
Results summary (frequ = frequency of self-improvement behavior):
The moderation effect of the frequency of self-improvement behaviors with the assertiveness change score (current-self) is not significantly different from zero, b = 0.071, p = 0.129.
6.6.2.5 Energy - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the energy change score (current-self) is not significantly different from zero, b = -0.001, p = 0.964.
6.6.2.6 Energy - frequency of self-improvement behaviors as moderator of change
Results summary (frequ = frequency of self-improvement behavior):
The moderation effect of the frequency of self-improvement behaviors with the energy change score (current-self) is not significantly different from zero, b = -0.05, p = 0.174.
6.6.2.7 Compassion - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the compassion change score (current-self) is not significantly different from zero, b = 0.017, p = 0.475.
6.6.2.8 Compassion - frequency of self-improvement behaviors as moderator of change
Results summary (frequ = frequency of self-improvement behavior):
The moderation effect of the facet-specific change goal with the respectfulness change score (current-self) is not significantly different from zero, b = 0.032, p = 0.162.
6.6.2.10 Respectfulness - frequency of self-improvement behaviors as moderator of change
Results summary (frequ = frequency of self-improvement behavior):
The moderation effect of the frequency of self-improvement behaviors with the respectfulness change score (current-self) is not significantly different from zero, b = 0.053, p = 0.196.
6.6.2.11 Trust - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the trust change score (current-self) is not significantly different from zero, b = -0.006, p = 0.786.
6.6.2.12 Trust - frequency of self-improvement behaviors as moderator of change
Results summary (frequ = frequency of self-improvement behavior):
The moderation effect of the self-improvement behaviors with the trust change score (current-self) is not significantly different from zero, b = -0.08, p = 0.125.
6.6.2.13 Organization - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the organization change score (current-self) is not significantly different from zero, b = -0.005, p = 0.883.
6.6.2.14 Organization - frequency of self-improvement behaviors as moderator of change
Results summary (frequ = frequency of self-improvement behavior):
The moderation effect of the frequency of self-improvement behaviors with the organization change score (current-self) is not significantly different from zero, b = -0.099, p = 0.106.
6.6.2.15 Productiveness - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the productiveness change score (current-self) is not significantly different from zero, b = -0.01, p = 0.722.
6.6.2.16 Productiveness - frequency of self-improvement behaviors as moderator of change
Results summary (frequ = frequency of self-improvement behavior):
The moderation effect of the facet-specific change goal with the responsibility change score (current-self) is not significantly different from zero, b = -0.005, p = 0.738.
6.6.2.18 Responsibility - frequency of self-improvement behaviors as moderator of change
Results summary (frequ = frequency of self-improvement behavior):
The moderation effect of the frequency of self-improvement behaviors with the responsibility change score (current-self) is not significantly different from zero, b = 0.041, p = 0.221.
6.6.2.19 Anxiety - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the anxiety change score (current-self) is not significantly different from zero, b = -0.069, p = 0.073.
6.6.2.20 Anxiety - frequency of self-improvement behaviors as moderator of change
Results summary (frequ = frequency of self-improvement behavior):
The moderation effect of the frequency of self-improvement behaviors with the anxiety change score (current-self) is not significantly different from zero, b = 0.072, p = 0.316.
6.6.2.21 Depression - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the depression change score (current-self) is not significantly different from zero, b = -0.036, p = 0.158.
6.6.2.22 Depression - frequency of self-improvement behaviors as moderator of change
Results summary (frequ = frequency of self-improvement behavior):
The moderation effect of the frequency of self-improvement behaviors with the depression change score (current-self) is not significantly different from zero, b = 0.022, p = 0.626.
6.6.2.23 Volatility - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the volatility change score (current-self) is not significantly different from zero, b = -0.012, p = 0.717.
6.6.2.24 Volatility - frequency of self-improvement behaviors as moderator of change
Results summary (frequ = frequency of self-improvement behavior):
The moderation effect of the frequency of self-improvement behaviors with the volatility change score (current-self) is significantly different from zero, b = -0.071, p = 0.256.
6.6.2.25 Curiosity - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
The moderation effect of the facet-specific change goal with the curiosity change score (current-self) is not significantly different from zero, b = 0.003, p = 0.924.
6.6.2.26 Curiosity - frequency of self-improvement behaviors as moderator of change
Results summary (frequ = frequency of self-improvement behavior):
The moderation effect of the frequency of self-improvement behaviors with the curiosity change score (current-self) is not significantly different from zero, b = 0.079, p = 0.125.
6.6.2.27 Aesthetic - specific, facet-level change goal as moderator of change
Results summary (sb07_xx_t1 = trait/facet specific change goal):
(here there were some convergence problems with the standard model that the loop tried to fit)
mi_lcs_aesth_curr_specif_hyp6 <-'aesth_t1 =~ 1*bf05_05_t1 + lamb2*bf05_20_t1 + lamb3*bf05_35_t1 + lamb4*bf05_50_t1 # This specifies the measurement model for aesth at T1aesth_t2 =~ 1*bf05_05_t2 + lamb2*bf05_20_t2 + lamb3*bf05_35_t2 + lamb4*bf05_50_t2 # This specifies the measurement model for aesth at T2 (with equality constraints)aesth_t2 ~ 1*aesth_t1 # This parameter regresses aesth_t2 perfectly on aesth_t1d_aesth_1 =~ 1*aesth_t2 # This defines the latent change score factor as measured perfectly by scores on aesth_t2aesth_t2 ~ 0*1 # This line constrains the intercept of aesth_t2 to 0aesth_t2 ~~ 0*aesth_t2 # This fixes the variance of aesth_t2 to 0d_aesth_1 ~ 1 # This estimates the intercept of the change score aesth_t1 ~ 1 # This estimates the intercept of aesth_t1 d_aesth_1 ~~ d_aesth_1 # This estimates the variance of the change scores aesth_t1 ~~ aesth_t1 # This estimates the variance of aesth_t1 aesth_t1 ~ sb07_14_t1 # This estimates the moderation effect on personality at T1d_aesth_1 ~ aesth_t1 + sb07_14_t1 # This estimates the self-feedback parameter and the moderation effect on the change scorebf05_05_t1 ~~ bf05_05_t2 # This allows residual covariance on indicator X1 across T1 and T2bf05_20_t1 ~~ bf05_20_t2 # This allows residual covariance on indicator X2 across T1 and T2bf05_35_t1 ~~ bf05_35_t2 # This allows residual covariance on indicator X3 across T1 and T2bf05_50_t1 ~~ bf05_50_t2 # This allows residual covariance on indicator X4 across T1 and T2bf05_05_t1 ~~ res1*bf05_05_t1 # This allows residual variance on indicator X1 at T1 bf05_20_t1 ~~ res2*bf05_20_t1 # This allows residual variance on indicator X2 at T1bf05_35_t1 ~~ res3*bf05_35_t1 # This allows residual variance on indicator X3 at T1bf05_50_t1 ~~ res4*bf05_50_t1 # This allows residual variance on indicator X4 at T1bf05_05_t2 ~~ res1*bf05_05_t2 # This allows residual variance on indicator X1 at T2 bf05_20_t2 ~~ res2*bf05_20_t2 # This allows residual variance on indicator X2 at T2 bf05_35_t2 ~~ res3*bf05_35_t2 # This allows residual variance on indicator X3 at T2bf05_50_t2 ~~ res4*bf05_50_t2 # This allows residual variance on indicator X4 at T2bf05_05_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1bf05_20_t1 ~ m2*1 # This estimates the intercept of X2 at T1bf05_35_t1 ~ m3*1 # This estimates the intercept of X3 at T1bf05_50_t1 ~ m4*1 # This estimates the intercept of X4 at T1bf05_05_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2bf05_20_t2 ~ m2*1 # This estimates the intercept of X2 at T2bf05_35_t2 ~ m3*1 # This estimates the intercept of X3 at T2bf05_50_t2 ~ m4*1 # This estimates the intercept of X4 at T2sb07_14_t1 ~~ sb07_14_t1sb07_14_t1 ~ 1'fit_mi_lcs_aesth_curr_specif_hyp6 <-lavaan(mi_lcs_aesth_curr_specif_hyp6, data=df_sbsa_wide_pers_sb %>%filter(!is.na(bf05_05_t1) &!is.na(bf05_05_t2)), estimator='mlr', fixed.x=FALSE, missing="fiml")# This model did not converge properly with missing data and FIML -> no problem when only using complete datasummary(fit_mi_lcs_aesth_curr_specif_hyp6, fit.measures=TRUE, standardized=TRUE, rsquare=F)
The facet-specific change goal significantly moderates changes in the aesthetic score (current-self), b = 0.003, p = 0.044.
6.6.2.28 Aesthetic - frequency of self-improvement behaviors as moderator of change
Results summary (frequ = frequency of self-improvement behavior):
(here there were some convergence problems with the standard model that the loop tried to fit)
mi_lcs_aesth_curr_frequ_hyp6 <-'aesth_t1 =~ 1*bf05_05_t1 + lamb2*bf05_20_t1 + lamb3*bf05_35_t1 + lamb4*bf05_50_t1 # This specifies the measurement model for aesth at T1aesth_t2 =~ 1*bf05_05_t2 + lamb2*bf05_20_t2 + lamb3*bf05_35_t2 + lamb4*bf05_50_t2 # This specifies the measurement model for aesth at T2 (with equality constraints)frequ =~ 1*sb04_01_t2 + sb04_02_t2 + sb04_03_t2 # latent variable for moderatoraesth_t2 ~ 1*aesth_t1 # This parameter regresses aesth_t2 perfectly on aesth_t1d_aesth_1 =~ 1*aesth_t2 # This defines the latent change score factor as measured perfectly by scores on aesth_t2aesth_t2 ~ 0*1 # This line constrains the intercept of aesth_t2 to 0aesth_t2 ~~ 0*aesth_t2 # This fixes the variance of aesth_t2 to 0d_aesth_1 ~ 1 # This estimates the intercept of the change score aesth_t1 ~ 1 # This estimates the intercept of aesth_t1 d_aesth_1 ~~ d_aesth_1 # This estimates the variance of the change scores aesth_t1 ~~ aesth_t1 # This estimates the variance of aesth_t1 aesth_t1 ~ frequ # This estimates the moderation effect on personality at T1d_aesth_1 ~ aesth_t1 + frequ # This estimates the self-feedback parameter and the moderation effect on the change scorefrequ ~ 0*1 # This fixes the intercept of the moderator to 0frequ ~~ frequ # This estimates the variance of the moderatorbf05_05_t1 ~~ bf05_05_t2 # This allows residual covariance on indicator X1 across T1 and T2bf05_20_t1 ~~ bf05_20_t2 # This allows residual covariance on indicator X2 across T1 and T2bf05_35_t1 ~~ bf05_35_t2 # This allows residual covariance on indicator X3 across T1 and T2bf05_50_t1 ~~ bf05_50_t2 # This allows residual covariance on indicator X4 across T1 and T2bf05_05_t1 ~~ res1*bf05_05_t1 # This allows residual variance on indicator X1 at T1 bf05_20_t1 ~~ res2*bf05_20_t1 # This allows residual variance on indicator X2 at T1bf05_35_t1 ~~ res3*bf05_35_t1 # This allows residual variance on indicator X3 at T1bf05_50_t1 ~~ res4*bf05_50_t1 # This allows residual variance on indicator X4 at T1bf05_05_t2 ~~ res1*bf05_05_t2 # This allows residual variance on indicator X1 at T2 bf05_20_t2 ~~ res2*bf05_20_t2 # This allows residual variance on indicator X2 at T2 bf05_35_t2 ~~ res3*bf05_35_t2 # This allows residual variance on indicator X3 at T2bf05_50_t2 ~~ res4*bf05_50_t2 # This allows residual variance on indicator X4 at T2bf05_05_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1bf05_20_t1 ~ m2*1 # This estimates the intercept of X2 at T1bf05_35_t1 ~ m3*1 # This estimates the intercept of X3 at T1bf05_50_t1 ~ m4*1 # This estimates the intercept of X4 at T1bf05_05_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2bf05_20_t2 ~ m2*1 # This estimates the intercept of X2 at T2bf05_35_t2 ~ m3*1 # This estimates the intercept of X3 at T2bf05_50_t2 ~ m4*1 # This estimates the intercept of X4 at T2sb04_01_t2 ~~ sb04_01_t2sb04_02_t2 ~~ sb04_02_t2sb04_03_t2 ~~ sb04_03_t2sb04_01_t2 ~ 1sb04_02_t2 ~ 1sb04_03_t2 ~ 1'fit_mi_lcs_aesth_curr_frequ_hyp6 <-lavaan(mi_lcs_aesth_curr_frequ_hyp6, data=df_sbsa_wide_pers_sb_mod %>%filter(!is.na(bf05_05_t1) &!is.na(bf05_05_t2)), estimator='mlr', fixed.x=FALSE, missing="fiml")# This model did not converge properly with missing data and FIML -> no problem when only using complete datasummary(fit_mi_lcs_aesth_curr_frequ_hyp6, fit.measures=TRUE, standardized=TRUE, rsquare=F)
The moderation effect of the facet-specific change goal with the imagination change score (current-self) is not significantly different from zero, b = 0.037, p = 0.118.
6.6.2.30 Imagination - frequency of self-improvement behaviors as moderator of change
Results summary (frequ = frequency of self-improvement behavior):
The moderation effect of the frequency of self-improvement behaviors with the imagination change score (current-self) is not significantly different from zero, b = 0.099, p = 0.086.
Results summary across the Big Five traits: trait-specific change goals (goals) and frequency of self-improvement behaviors (frequency) as moderators on the latent change score. *_main* refers to the main effects (change scores) and *_mod* to the moderation effects.
kable(df_table_hyp6[1:10, ], digits =3)
trait
moderator
est_main
std_main
p_main
est_mod
std_mod
p_mod
extraversion
goals
0.532
1.511
0.001
0.063
0.128
0.324
extraversion
frequency
0.703
2.021
0.000
0.076
0.153
0.090
agreeableness
goals
0.655
3.090
0.000
0.022
0.090
0.362
agreeableness
frequency
0.778
3.592
0.000
0.073
0.243
0.016
conscientiousness
goals
0.662
1.845
0.000
0.002
0.008
0.936
conscientiousness
frequency
0.714
1.972
0.000
0.090
0.176
0.018
neuroticism
goals
0.808
1.622
0.000
0.085
0.198
0.071
neuroticism
frequency
0.531
1.080
0.000
-0.066
-0.096
0.200
openness
goals
-0.076
-0.389
0.627
0.103
0.314
0.007
openness
frequency
0.161
0.828
0.287
0.075
0.271
0.028
Four moderator effects significantly differ from zero:
changes in current-level agreeableness are moderated by the frequency of self-improvement behaviors
changes in current-level conscientiousness are moderated by the frequency of self-improvement behaviors
changes in current-level openness are moderated by the trait-specific change goals
changes in current-level openness are moderated by the frequency of self-improvement behaviors
Results summary across the Big Five facets: trait-specific change goals (goals) and frequency of self-improvement behaviors (frequency) as moderators on the latent change score. *_main* refers to the main effects (change scores) and *_mod* to the moderation effects.
kable(df_table_hyp6[11:40, ], digits =3)
trait
moderator
est_main
std_main
p_main
est_mod
std_mod
p_mod
sociability
goals
0.832
1.560
0.000
-0.003
-0.008
0.920
sociability
frequency
0.829
1.578
0.000
0.042
0.057
0.503
assertiveness
goals
0.172
0.683
0.428
0.030
0.136
0.248
assertiveness
frequency
0.360
1.473
0.035
0.071
0.206
0.129
energy
goals
0.332
1.603
0.021
-0.001
-0.004
0.964
energy
frequency
0.369
1.697
0.016
-0.050
-0.160
0.174
compassion
goals
0.759
3.028
0.009
0.017
0.096
0.475
compassion
frequency
0.947
3.805
0.000
0.127
0.369
0.010
respectfulness
goals
1.019
3.062
0.001
0.032
0.130
0.162
respectfulness
frequency
1.263
3.870
0.000
0.053
0.116
0.196
trust
goals
0.399
1.224
0.048
-0.006
-0.026
0.786
trust
frequency
0.422
1.310
0.032
-0.080
-0.178
0.125
organization
goals
0.417
0.778
0.000
-0.005
-0.015
0.883
organization
frequency
0.462
0.897
0.000
-0.099
-0.138
0.106
productiveness
goals
0.411
1.088
0.009
-0.010
-0.034
0.722
productiveness
frequency
0.447
1.195
0.006
-0.108
-0.204
0.029
responsibility
goals
0.434
2.121
0.082
-0.005
-0.038
0.738
responsibility
frequency
0.377
1.813
0.050
0.041
0.141
0.221
anxiety
goals
1.403
2.502
0.000
-0.069
-0.187
0.073
anxiety
frequency
0.948
1.661
0.000
0.072
0.090
0.316
depression
goals
0.908
2.523
0.001
-0.036
-0.160
0.158
depression
frequency
0.587
1.605
0.000
0.022
0.042
0.626
volatility
goals
0.578
1.115
0.000
-0.012
-0.030
0.717
volatility
frequency
0.560
1.080
0.000
-0.071
-0.097
0.256
curiosity
goals
0.364
1.821
0.246
0.003
0.016
0.924
curiosity
frequency
0.453
2.259
0.077
0.079
0.277
0.125
aesthetic
goals
0.457
17.231
0.006
0.003
0.144
0.044
aesthetic
frequency
0.533
22.761
0.003
0.007
0.213
0.023
imagination
goals
0.224
0.639
0.385
0.037
0.150
0.118
imagination
frequency
0.492
1.400
0.034
0.099
0.197
0.086
Looking at the facets, we find three moderator effects that significantly differ from zero:
Within agreeableness, we find the effect for the frequency of self-improvement behaviors from above only represented in the facet compassion.
The effect for conscientiousness is represented in one of the three facets, productiveness.
The effect seen above for openness is mirrored in the aesthetic score with both the change goal and the frequency of self-improvement behaviors.
6.7 H7: Desire to change and frequency of self-acceptance behaviors as moderators of change in personality in self-acceptance group (H3 in paper)
Desire to change and frequency of self-acceptance behaviors measured at the follow-up assessment will be positively related to change in ideal-self ratings in the self-acceptance group.
To test this hypothesis, we will estimate the mean-level difference in ideal trait ratings between baseline and follow up using a latent change model for each big five domain and facet. We will then include two moderators. The first will indicate how much the individual wanted to accept themselves on a given big five domain or facet. The second will indicate their frequency of self-acceptance behaviors. We will estimate the main effects of each of these variables and the interaction between these variables on the trait change score.
# create templates:# 1st, for facet-specific acceptance goalstrait_template_mod_goal_accept <-'trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for trait_t1 trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadingsgoals =~ 1*ind_goal_1 + ind_goal_2 + ind_goal_3 # latent variable for moderatortrait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0d_trait_1 ~ 1 # This estimates the intercept of the change score trait_t1 ~ 1 # This estimates the intercept of trait_t1 d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1 trait_t1 ~ goals # This estimates the moderation effect on personality at T1d_trait_1 ~ trait_t1 + goals # This estimates the self-feedback parameter and the moderation effect on the change scoregoals ~ 0*1 # This fixes the intercept of the moderator to 0goals ~~ goals # This estimates the variance of the moderatorind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1 ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2 ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2 ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2ind_goal_1 ~~ ind_goal_1ind_goal_2 ~~ ind_goal_2ind_goal_3 ~~ ind_goal_3ind_goal_1 ~ 1ind_goal_2 ~ 1ind_goal_3 ~ 1'trait_facets_nrs <-list(a1 =c(1:3), b2 =c(4:6), c3 =c(7:9), d4 =c(10:12), e5 =c(13:15)) # matching facet nrs to traits # loop across 5 traitsfor (i in1:5) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post ideal (6 = ideal)# items = paste0(bfi_versions[[6]], item_nrs) # using parcels instead! mod_names =paste0("sa07_", str_pad(trait_facets_nrs[[i]], 2, pad ="0"), "_t1") template_filled <-str_replace_all(trait_template_mod_goal_accept, c("trait"= short_name,"ind01"=paste0(short_name, "_ideal_par1"), "ind02"=paste0(short_name, "_ideal_par2"), "ind03"=paste0(short_name, "_ideal_par3"),"ind_goal_1"= mod_names[1], "ind_goal_2"= mod_names[2], "ind_goal_3"= mod_names[3])) trait_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_specif_hyp7")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_specif_hyp7")), trait_model_fit))} # 2nd, for frequency of self-acceptance behaviortrait_template_mod_frequ_accept <-'trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for extra_t1 trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for extra_t2 with the equality constrained factor loadingsfrequ =~ 1*sa04_01_t2 + sa04_02_t2 + sa04_03_t2 # latent variable for moderatortrait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0d_trait_1 ~ 1 # This estimates the intercept of the change score trait_t1 ~ 1 # This estimates the intercept of trait_t1 d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1 trait_t1 ~ frequ # This estimates the moderation effect on personality at T1d_trait_1 ~ trait_t1 + frequ # This estimates the self-feedback parameter and the moderation effect on the change scorefrequ ~ 0*1 # This fixes the intercept of the moderator to 0frequ ~~ frequ # This estimates the variance of the moderatorind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1 ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2 ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2 ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2sa04_01_t2 ~~ sa04_01_t2sa04_02_t2 ~~ sa04_02_t2sa04_03_t2 ~~ sa04_03_t2sa04_01_t2 ~ 1sa04_02_t2 ~ 1sa04_03_t2 ~ 1'# loop across 5 traitsfor (i in1:5) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post ideal# items = paste0(bfi_versions[[6]], item_nrs) # using parcels instead! template_filled <-str_replace_all(trait_template_mod_frequ_accept, c("trait"= short_name,"ind01"=paste0(short_name, "_ideal_par1"), "ind02"=paste0(short_name, "_ideal_par2"), "ind03"=paste0(short_name, "_ideal_par3"))) trait_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7")), trait_model_fit))}
6.7.1.1 Extraversion: specific, facet-level acceptance goals as moderator of change
Results summary (goals = trait/facet specific acceptance goal):
The moderation effect of specific, facet-level acceptance goals with the extraversion change score (ideal-self) is not significantly different from zero, b = -0.037, p = 0.38.
6.7.1.2 Extraversion: frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self improvement behavior):
The moderation effect of the frequency of self-acceptance behaviors with the extraversion change score (ideal-self) is not significantly different from zero, b = 0.056, p = 0.153.
6.7.1.3 Agreeableness: specific, facet-level acceptance goals as moderator of change
Results summary (goals = trait/facet specific acceptance goal):
The moderation effect of specific, facet-level acceptance goals with the agreeableness change score (ideal-self) is not significantly different from zero, b = 0.022, p = 0.598.
6.7.1.4 Agreeableness: frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self improvement behavior):
The moderation effect of specific, facet-level acceptance goals with the conscientiousness change score (ideal-self) is not significantly different from zero, b = -0.02, p = 0.33.
6.7.1.6 Conscientiousness: frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self improvement behavior):
The moderation effect of specific, facet-level acceptance goals with the neuroticism change score (ideal-self) is not significantly different from zero, b = 0.056, p = 0.119.
6.7.1.8 Neuroticism: frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self improvement behavior):
The moderation effect of the frequency of self-acceptance behaviors with the neuroticism change score (ideal-self) is not significantly different from zero, b = -0.043, p = 0.19.
6.7.1.9 Openness: specific, facet-level acceptance goals as moderator of change
Results summary (goals = trait/facet specific acceptance goal):
The moderation effect of specific, facet-level acceptance goals with the openness change score (ideal-self) is not significantly different from zero, b = 0.007, p = 0.811.
6.7.1.10 Openness: frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self improvement behavior):
The moderation effect of frequency of self-acceptance behaviors with the openness change score (ideal-self) is not significantly different from zero, b = 0.037, p = 0.194.
6.7.2 Big Five facets
Run models for all facets with a template & loop:
Show the code
# create templates:# 1st, for facet-specific acceptance goalfacet_template_mod_goal_accept <-'facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0d_facet_1 ~ 1 # This estimates the intercept of the change score facet_t1 ~ 1 # This estimates the intercept of facet_t1 d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1 facet_t1 ~ ind_goal # This estimates the moderation effect on personality at T1d_facet_1 ~ facet_t1 + ind_goal # This estimates the self-feedback parameter and the moderation effect on the change scoreind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1 ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2 ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2 ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2ind_goal ~~ ind_goalind_goal ~ 1'# loop across 15 facetsfor (i in6:length(b5_vars)) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post ideal items =paste0(bfi_versions[[6]], item_nrs) mod_name =paste0("sa07_", str_pad(i-5, 2, pad ="0"), "_t1") template_filled <-str_replace_all(facet_template_mod_goal_accept, c("facet"= short_name,"ind1"= items[1], "ind2"= items[2], "ind3"= items[3], "ind4"= items[4],"ind_goal"= mod_name)) facet_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_specif_hyp7")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_specif_hyp7")), facet_model_fit))} # 2nd, for frequency of self-acceptance behaviorfacet_template_mod_frequ_accept <-'facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)frequ =~ 1*sa04_01_t2 + sa04_02_t2 + sa04_03_t2 # latent variable for moderatorfacet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0d_facet_1 ~ 1 # This estimates the intercept of the change score facet_t1 ~ 1 # This estimates the intercept of facet_t1 d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1 facet_t1 ~ frequ # This estimates the moderation effect on personality at T1d_facet_1 ~ facet_t1 + frequ # This estimates the self-feedback parameter and the moderation effect on the change scorefrequ ~ 0*1 # This fixes the intercept of the moderator to 0frequ ~~ frequ # This estimates the variance of the moderatorind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1 ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2 ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2 ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2sa04_01_t2 ~~ sa04_01_t2sa04_02_t2 ~~ sa04_02_t2sa04_03_t2 ~~ sa04_03_t2sa04_01_t2 ~ 1sa04_02_t2 ~ 1sa04_03_t2 ~ 1'# loop across 15 facetsfor (i in6:length(b5_vars)) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post ideal items =paste0(bfi_versions[[6]], item_nrs) template_filled <-str_replace_all(facet_template_mod_frequ_accept, c("facet"= short_name,"ind1"= items[1], "ind2"= items[2], "ind3"= items[3], "ind4"= items[4])) facet_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sa_mod, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_frequ_hyp7")), facet_model_fit))}
6.7.2.1 Sociability - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the sociability change score (ideal-self) is not significantly different from zero, b = -0.008, p = 0.648.
6.7.2.2 Sociability - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with sociability change score (ideal-self) is not significantly different from zero, b = -0.03, p = 0.35.
6.7.2.3 Assertiveness - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the assertiveness change score (ideal-self) is not significantly different from zero, b = -0.014, p = 0.261.
6.7.2.4 Assertiveness - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the facet-specific acceptance goal with the energy change score (ideal-self) is not significantly different from zero, b = 0.025, p = 0.112.
6.7.2.6 Energy - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the energy change score (ideal-self) is not significantly different from zero, b = -0.059, p = 0.137.
6.7.2.7 Compassion - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the compassion change score (ideal-self) is not significantly different from zero, b = -0.051, p = 0.058.
6.7.2.8 Compassion - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the compassion change score (ideal-self) is not significantly different from zero, b = -0.03, p = 0.673.
6.7.2.9 Respectfulness - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the respectfulness change score (ideal-self) is not significantly different from zero, b = 0.003, p = 0.852.
6.7.2.10 Respectfulness - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the facet-specific acceptance goal with the trust change score (ideal-self) is not significantly different from zero, b = 0.01, p = 0.6.
6.7.2.12 Trust - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the trust change score (ideal-self) is not significantly different from zero, b = -0.07, p = 0.12.
6.7.2.13 Organization - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the organization change score (ideal-self) is not significantly different from zero, b = 0.003, p = 0.745.
6.7.2.14 Organization - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the facet-specific acceptance goal with the productiveness change score (ideal-self) is not significantly different from zero, b = 0.001, p = 0.924.
6.7.2.16 Productiveness - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the facet-specific acceptance goal with the responsibility change score (ideal-self) is not significantly different from zero, b = -0.017, p = 0.424.
6.7.2.18 Responsibility - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the responsibility change score (ideal-self) is not significantly different from zero, b = 0.073, p = 0.126.
6.7.2.19 Anxiety - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the anxiety change score (ideal-self) is not significantly different from zero, b = 0.003, p = 0.885.
6.7.2.20 Anxiety - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the anxiety change score (ideal-self) is not significantly different from zero, b = 0.006, p = 0.892.
6.7.2.21 Depression - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the depression change score (ideal-self) is not significantly different from zero, b = 0.005, p = 0.704.
6.7.2.22 Depression - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the depression change score (ideal-self) is not significantly different from zero, b = 0.033, p = 0.206.
6.7.2.23 Volatility - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the volatility change score (ideal-self) is not significantly different from zero, b = -0.021, p = 0.349.
6.7.2.24 Volatility - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the volatility change score (ideal-self) is significantly different from zero, b = -0.075, p = 0.101.
6.7.2.25 Curiosity - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the curiosity change score (ideal-self) is not significantly different from zero, b = -0.018, p = 0.276.
6.7.2.26 Curiosity - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the curiosity change score (ideal-self) is not significantly different from zero, b = 0.033, p = 0.326.
6.7.2.27 Aesthetic - specific, facet-level acceptance goal as moderator of change
Results summary (sa07_xx_t1 = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the aesthetic change score (ideal-self) is not significantly different from zero, b = 0, p = 0.737.
6.7.2.28 Aesthetic - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the aesthetic change score (ideal-self) is not significantly different from zero, b = 0.006, p = 0.062.
6.7.2.29 Imagination - specific, facet-level acceptance goal as moderator of change
Results summary (*sa07_$$_t1* = trait/facet specific acceptance goal):
The moderation effect of the facet-specific acceptance goal with the imagination change score (ideal-self) is not significantly different from zero, b = 0.013, p = 0.4.
6.7.2.30 Imagination - frequency of self-acceptance behaviors as moderator of change
Results summary (frequ = frequency of self-acceptance behavior):
The moderation effect of the frequency of self-acceptance behaviors with the imagination change score (ideal-self) is not significantly different from zero, b = 0.011, p = 0.795.
Results summary across the Big Five traits: trait-specific acceptance goals (goals) and frequency of self-acceptance behaviors (frequency) as moderators on the latent change score. *_main* refers to the main effects (change scores) and *_mod* to the moderation effects.
kable(df_table_hyp7[1:10, ], digits =3)
trait
moderator
est_main
std_main
p_main
est_mod
std_mod
p_mod
extraversion
goals
1.513
3.831
0.000
-0.037
-0.087
0.380
extraversion
frequency
1.579
4.107
0.000
0.056
0.110
0.153
agreeableness
goals
0.793
2.485
0.000
0.022
0.050
0.598
agreeableness
frequency
0.999
3.188
0.000
0.090
0.218
0.012
conscientiousness
goals
1.323
3.809
0.003
-0.020
-0.065
0.330
conscientiousness
frequency
1.465
4.200
0.001
0.088
0.189
0.030
neuroticism
goals
0.469
1.449
0.000
0.056
0.139
0.119
neuroticism
frequency
0.493
1.504
0.000
-0.043
-0.101
0.190
openness
goals
0.674
2.854
0.010
0.007
0.021
0.811
openness
frequency
0.738
3.116
0.005
0.037
0.119
0.194
Two moderator effects that are significantly different from zero:
The frequency of self-acceptance behaviors moderates changes in ideal-level agreeableness.
Frequency of self-acceptance behaviors also moderates changes in ideal-level conscientiousness.
Results summary across the Big Five facets: trait-specific acceptance goals (goals) and frequency of self-acceptance behaviors (frequency) as moderators on the latent change score. *_main* refers to the main effects (change scores) and *_mod* to the moderation effects.
kable(df_table_hyp7[11:40, ], digits =3)
trait
moderator
est_main
std_main
p_main
est_mod
std_mod
p_mod
sociability
goals
1.235
4.845
0.001
-0.008
-0.045
0.648
sociability
frequency
1.242
4.934
0.001
-0.030
-0.090
0.350
assertiveness
goals
1.167
5.727
0.003
-0.014
-0.098
0.261
assertiveness
frequency
1.313
6.473
0.001
0.074
0.272
0.015
energy
goals
0.935
3.950
0.004
0.025
0.146
0.112
energy
frequency
1.146
5.041
0.002
-0.059
-0.198
0.137
compassion
goals
0.883
2.637
0.033
-0.051
-0.198
0.058
compassion
frequency
0.666
1.954
0.145
-0.030
-0.067
0.673
respectfulness
goals
0.889
3.744
0.087
0.003
0.019
0.852
respectfulness
frequency
1.231
5.155
0.018
0.094
0.297
0.012
trust
goals
0.369
1.307
0.029
0.010
0.050
0.600
trust
frequency
0.470
1.614
0.005
-0.070
-0.183
0.120
organization
goals
0.593
2.811
0.000
0.003
0.023
0.745
organization
frequency
0.738
3.758
0.000
-0.084
-0.318
0.005
productiveness
goals
0.467
1.726
0.012
0.001
0.007
0.924
productiveness
frequency
0.516
1.917
0.008
-0.090
-0.254
0.018
responsibility
goals
1.497
3.991
0.002
-0.017
-0.064
0.424
responsibility
frequency
1.571
4.167
0.001
0.073
0.146
0.126
anxiety
goals
0.949
3.719
0.318
0.003
0.018
0.885
anxiety
frequency
0.897
3.662
0.383
0.006
0.020
0.892
depression
goals
1.528
6.872
0.010
0.005
0.031
0.704
depression
frequency
1.579
6.987
0.006
0.033
0.113
0.206
volatility
goals
0.358
1.130
0.080
-0.021
-0.088
0.349
volatility
frequency
0.353
1.170
0.074
-0.075
-0.188
0.101
curiosity
goals
1.265
6.356
0.003
-0.018
-0.122
0.276
curiosity
frequency
1.217
6.071
0.006
0.033
0.126
0.326
aesthetic
goals
0.619
24.722
0.045
0.000
-0.025
0.737
aesthetic
frequency
0.703
28.623
0.029
0.006
0.177
0.062
imagination
goals
0.098
0.478
0.880
0.013
0.091
0.400
imagination
frequency
0.184
0.874
0.787
0.011
0.038
0.795
Several moderator effects that are significantly different from zero:
The frequency of self-acceptance behaviors moderates changes in ideal-level assertiveness.
The effect for agreeableness (frequency of self-acceptance behaviors) is only represented within the respectfulness facet.
The effect for conscientiousness (frequency of self-acceptance behaviors) is represented within the organization and productiveness facets.
7.1 Differences in change across experimental groups (a)
We will explore a) whether change in psychological well-being indicators as well as the difference between real- and ideal- self will differ across groups at follow-up.
7.1.1 Well-being change: differences across groups
7.1.1.1 Life satisfaction
Life satisfaction: fitting multi-group models
Show the code
# adapt latent change score model from above and add grouping factor in estimation (also add vectorized equality constraints to the model)# configural invariancemi_lcs_swls_group_config <-'swls_t1 =~ 1*sw06_01_t1 + c("lamb2a", "lamb2b")*sw06_02_t1 + c("lamb3a", "lamb3b")*sw06_03_t1 + c("lamb4a", "lamb4b")*sw06_04_t1 # This specifies the measurement model for swls_t1 swls_t2 =~ 1*sw06_01_t2 + c("lamb2a", "lamb2b")*sw06_02_t2 + c("lamb3a", "lamb3b")*sw06_03_t2 + c("lamb4a", "lamb4b")*sw06_04_t2 # This specifies the measurement model for swls_t2 with the equality constrained factor loadingsswls_t2 ~ 1*swls_t1 # This parameter regresses swls_t2 perfectly on swls_t1d_swls_1 =~ 1*swls_t2 # This defines the latent change score factor as measured perfectly by scores on swls_t2swls_t2 ~ 0*1 # This line constrains the intercept of swls_t2 to 0swls_t2 ~~ 0*swls_t2 # This fixes the variance of swls_t2 to 0d_swls_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score swls_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of swls_t1 d_swls_1 ~~ c("d_var_a", "d_var_b")*d_swls_1 # This estimates the variance of the change scores swls_t1 ~~ c("wb_var_a", "wb_var_b")*swls_t1 # This estimates the variance of the swls_t1 d_swls_1 ~ c("fb_a", "fb_b")*swls_t1 # This estimates the self-feedback parametersw06_01_t1 ~~ c("cov1a", "cov1b")*sw06_01_t2 # This allows residual covariance on indicator X1 across T1 and T2sw06_02_t1 ~~ c("cov2a", "cov2b")*sw06_02_t2 # This allows residual covariance on indicator X2 across T1 and T2sw06_03_t1 ~~ c("cov3a", "cov3b")*sw06_03_t2 # This allows residual covariance on indicator X3 across T1 and T2sw06_04_t1 ~~ c("cov4a", "cov4b")*sw06_04_t2 # This allows residual covariance on indicator X4 across T1 and T2sw06_01_t1 ~~ c("res1a", "res1b")*sw06_01_t1 # This allows residual variance on indicator X1 at T1 sw06_02_t1 ~~ c("res2a", "res2b")*sw06_02_t1 # This allows residual variance on indicator X2 at T1sw06_03_t1 ~~ c("res3a", "res3b")*sw06_03_t1 # This allows residual variance on indicator X3 at T1sw06_04_t1 ~~ c("res4a", "res4b")*sw06_04_t1 # This allows residual variance on indicator X4 at T1sw06_01_t2 ~~ c("res1a", "res1b")*sw06_01_t2 # This allows residual variance on indicator X1 at T2 sw06_02_t2 ~~ c("res2a", "res2b")*sw06_02_t2 # This allows residual variance on indicator X2 at T2 sw06_03_t2 ~~ c("res3a", "res3b")*sw06_03_t2 # This allows residual variance on indicator X3 at T2sw06_04_t2 ~~ c("res4a", "res4b")*sw06_04_t2 # This allows residual variance on indicator X4 at T2sw06_01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1sw06_02_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1sw06_03_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1sw06_04_t1 ~ c("m4a", "m4b")*1 # This estimates the intercept of X4 at T1sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2sw06_02_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2sw06_03_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2sw06_04_t2 ~ c("m4a", "m4b")*1 # This estimates the intercept of X4 at T2'lcs_swls_group_config <-sem(mi_lcs_swls_group_config, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando")# weak invariancemi_lcs_swls_group_weak <-'swls_t1 =~ 1*sw06_01_t1 + c("lamb2", "lamb2")*sw06_02_t1 + c("lamb3", "lamb3")*sw06_03_t1 + c("lamb4", "lamb4")*sw06_04_t1 # This specifies the measurement model for swls_t1 swls_t2 =~ 1*sw06_01_t2 + c("lamb2", "lamb2")*sw06_02_t2 + c("lamb3", "lamb3")*sw06_03_t2 + c("lamb4", "lamb4")*sw06_04_t2 # This specifies the measurement model for swls_t2 with the equality constrained factor loadingsswls_t2 ~ 1*swls_t1 # This parameter regresses swls_t2 perfectly on swls_t1d_swls_1 =~ 1*swls_t2 # This defines the latent change score factor as measured perfectly by scores on swls_t2swls_t2 ~ 0*1 # This line constrains the intercept of swls_t2 to 0swls_t2 ~~ 0*swls_t2 # This fixes the variance of swls_t2 to 0d_swls_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score swls_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of swls_t1 d_swls_1 ~~ c("d_var_a", "d_var_b")*d_swls_1 # This estimates the variance of the change scores swls_t1 ~~ c("wb_var_a", "wb_var_b")*swls_t1 # This estimates the variance of the swls_t1 d_swls_1 ~ c("fb_a", "fb_b")*swls_t1 # This estimates the self-feedback parametersw06_01_t1 ~~ c("cov1a", "cov1b")*sw06_01_t2 # This allows residual covariance on indicator X1 across T1 and T2sw06_02_t1 ~~ c("cov2a", "cov2b")*sw06_02_t2 # This allows residual covariance on indicator X2 across T1 and T2sw06_03_t1 ~~ c("cov3a", "cov3b")*sw06_03_t2 # This allows residual covariance on indicator X3 across T1 and T2sw06_04_t1 ~~ c("cov4a", "cov4b")*sw06_04_t2 # This allows residual covariance on indicator X4 across T1 and T2sw06_01_t1 ~~ c("res1a", "res1b")*sw06_01_t1 # This allows residual variance on indicator X1 at T1 sw06_02_t1 ~~ c("res2a", "res2b")*sw06_02_t1 # This allows residual variance on indicator X2 at T1sw06_03_t1 ~~ c("res3a", "res3b")*sw06_03_t1 # This allows residual variance on indicator X3 at T1sw06_04_t1 ~~ c("res4a", "res4b")*sw06_04_t1 # This allows residual variance on indicator X4 at T1sw06_01_t2 ~~ c("res1a", "res1b")*sw06_01_t2 # This allows residual variance on indicator X1 at T2 sw06_02_t2 ~~ c("res2a", "res2b")*sw06_02_t2 # This allows residual variance on indicator X2 at T2 sw06_03_t2 ~~ c("res3a", "res3b")*sw06_03_t2 # This allows residual variance on indicator X3 at T2sw06_04_t2 ~~ c("res4a", "res4b")*sw06_04_t2 # This allows residual variance on indicator X4 at T2sw06_01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1sw06_02_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1sw06_03_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1sw06_04_t1 ~ c("m4a", "m4b")*1 # This estimates the intercept of X4 at T1sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2sw06_02_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2sw06_03_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2sw06_04_t2 ~ c("m4a", "m4b")*1 # This estimates the intercept of X4 at T2'lcs_swls_group_weak <-sem(mi_lcs_swls_group_weak, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando", group.equal ="loadings")# strong invariancemi_lcs_swls_group_strong <-'swls_t1 =~ 1*sw06_01_t1 + c("lamb2", "lamb2")*sw06_02_t1 + c("lamb3", "lamb3")*sw06_03_t1 + c("lamb4", "lamb4")*sw06_04_t1 # This specifies the measurement model for swls_t1 swls_t2 =~ 1*sw06_01_t2 + c("lamb2", "lamb2")*sw06_02_t2 + c("lamb3", "lamb3")*sw06_03_t2 + c("lamb4", "lamb4")*sw06_04_t2 # This specifies the measurement model for swls_t2 with the equality constrained factor loadingsswls_t2 ~ 1*swls_t1 # This parameter regresses swls_t2 perfectly on swls_t1d_swls_1 =~ 1*swls_t2 # This defines the latent change score factor as measured perfectly by scores on swls_t2swls_t2 ~ 0*1 # This line constrains the intercept of swls_t2 to 0swls_t2 ~~ 0*swls_t2 # This fixes the variance of swls_t2 to 0d_swls_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score swls_t1 ~ c("wb_int", "wb_int")*1 # This estimates the intercept of swls_t1 d_swls_1 ~~ c("d_var_a", "d_var_b")*d_swls_1 # This estimates the variance of the change scores swls_t1 ~~ c("wb_var_a", "wb_var_b")*swls_t1 # This estimates the variance of the swls_t1 d_swls_1 ~ c("fb_a", "fb_b")*swls_t1 # This estimates the self-feedback parametersw06_01_t1 ~~ c("cov1a", "cov1b")*sw06_01_t2 # This allows residual covariance on indicator X1 across T1 and T2sw06_02_t1 ~~ c("cov2a", "cov2b")*sw06_02_t2 # This allows residual covariance on indicator X2 across T1 and T2sw06_03_t1 ~~ c("cov3a", "cov3b")*sw06_03_t2 # This allows residual covariance on indicator X3 across T1 and T2sw06_04_t1 ~~ c("cov4a", "cov4b")*sw06_04_t2 # This allows residual covariance on indicator X4 across T1 and T2sw06_01_t1 ~~ c("res1a", "res1b")*sw06_01_t1 # This allows residual variance on indicator X1 at T1 sw06_02_t1 ~~ c("res2a", "res2b")*sw06_02_t1 # This allows residual variance on indicator X2 at T1sw06_03_t1 ~~ c("res3a", "res3b")*sw06_03_t1 # This allows residual variance on indicator X3 at T1sw06_04_t1 ~~ c("res4a", "res4b")*sw06_04_t1 # This allows residual variance on indicator X4 at T1sw06_01_t2 ~~ c("res1a", "res1b")*sw06_01_t2 # This allows residual variance on indicator X1 at T2 sw06_02_t2 ~~ c("res2a", "res2b")*sw06_02_t2 # This allows residual variance on indicator X2 at T2 sw06_03_t2 ~~ c("res3a", "res3b")*sw06_03_t2 # This allows residual variance on indicator X3 at T2sw06_04_t2 ~~ c("res4a", "res4b")*sw06_04_t2 # This allows residual variance on indicator X4 at T2sw06_01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1sw06_02_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1sw06_03_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1sw06_04_t1 ~ c("m4", "m4")*1 # This estimates the intercept of X4 at T1sw06_01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2sw06_02_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2sw06_03_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2sw06_04_t2 ~ c("m4", "m4")*1 # This estimates the intercept of X4 at T2'lcs_swls_group_strong <-sem(mi_lcs_swls_group_strong, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando", group.equal =c("intercepts", "loadings"))
Life satisfaction: results
# model comparison tests for measurement invariancelavTestLRT(lcs_swls_group_config, lcs_swls_group_weak, lcs_swls_group_strong)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
lcs_swls_group_config 50 14267 14441 93.624
lcs_swls_group_weak 53 14269 14429 101.697 9.1169 3 0.02778 *
lcs_swls_group_strong 58 14264 14401 106.064 4.4012 5 0.49321
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# show model with varying latent change parameters # -> key parameter is "d_swls_1 ~1"# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)kable(broom::tidy(lcs_swls_group_weak, conf.int =TRUE, conf.level =0.95) %>%select(term, label, estimate, std.all, statistic, p.value) %>%filter(term %in%c("d_swls_1 ~1 ")), digits =3)
term
label
estimate
std.all
statistic
p.value
d_swls_1 ~1
d_int_a
0.853
1.297
6.040
0
d_swls_1 ~1
d_int_b
0.669
1.004
4.835
0
# constrained to be equal in the strong measurement invariance model:kable(broom::tidy(lcs_swls_group_strong, conf.int =TRUE, conf.level =0.95) %>%select(term, label, estimate, std.all, statistic, p.value) %>%filter(term %in%c("d_swls_1 ~1 ")), digits =3)
term
label
estimate
std.all
statistic
p.value
d_swls_1 ~1
d_int
0.767
1.186
7.817
0
d_swls_1 ~1
d_int
0.767
1.133
7.817
0
Slightly more positive change in life satisfaction in the Self-Acceptance group but no substantial differences according to the LRTs.
# whole model (weak invariance)summary(lcs_swls_group_weak, fit.measures=TRUE, standardized=TRUE, rsquare=F)
# adapt latent change score model from above and add grouping factor in estimation (also add vectorized equality constraints to the model)# configural invariancemi_lcs_meaning_group_config <-'meaning_t1 =~ 1*meaning_par1_t1 + c("lamb2a", "lamb2b")*meaning_par2_t1 + c("lamb3a", "lamb3b")*meaning_par3_t1 # This specifies the measurement model for meaning_t1 meaning_t2 =~ 1*meaning_par1_t2 + c("lamb2a", "lamb2b")*meaning_par2_t2 + c("lamb3a", "lamb3b")*meaning_par3_t2 # This specifies the measurement model for meaning_t2 with the equality constrained factor loadingsmeaning_t2 ~ 1*meaning_t1 # This parameter regresses meaning_t2 perfectly on meaning_t1d_meaning_1 =~ 1*meaning_t2 # This defines the latent change score factor as measured perfectly by scores on meaning_t2meaning_t2 ~ 0*1 # This line constrains the intercept of meaning_t2 to 0meaning_t2 ~~ 0*meaning_t2 # This fixes the variance of meaning_t2 to 0d_meaning_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score meaning_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of meaning_t1 d_meaning_1 ~~ c("d_var_a", "d_var_b")*d_meaning_1 # This estimates the variance of the change scores meaning_t1 ~~ c("wb_var_a", "wb_var_b")*meaning_t1 # This estimates the variance of the meaning_t1 d_meaning_1 ~ c("fb_a", "fb_b")*meaning_t1 # This estimates the self-feedback parametermeaning_par1_t1 ~~ c("cov1a", "cov1b")*meaning_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2meaning_par2_t1 ~~ c("cov2a", "cov2b")*meaning_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2meaning_par3_t1 ~~ c("cov3a", "cov3b")*meaning_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2meaning_par1_t1 ~~ c("res1a", "res1b")*meaning_par1_t1 # This allows residual variance on indicator X1 at T1 meaning_par2_t1 ~~ c("res2a", "res2b")*meaning_par2_t1 # This allows residual variance on indicator X2 at T1meaning_par3_t1 ~~ c("res3a", "res3b")*meaning_par3_t1 # This allows residual variance on indicator X3 at T1meaning_par1_t2 ~~ c("res1a", "res1b")*meaning_par1_t2 # This allows residual variance on indicator X1 at T2 meaning_par2_t2 ~~ c("res2a", "res2b")*meaning_par2_t2 # This allows residual variance on indicator X2 at T2 meaning_par3_t2 ~~ c("res3a", "res3b")*meaning_par3_t2 # This allows residual variance on indicator X3 at T2meaning_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1meaning_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1meaning_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2meaning_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2meaning_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2'lcs_meaning_group_config <-sem(mi_lcs_meaning_group_config, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando")# weak invariancemi_lcs_meaning_group_weak <-'meaning_t1 =~ 1*meaning_par1_t1 + c("lamb2", "lamb2")*meaning_par2_t1 + c("lamb3", "lamb3")*meaning_par3_t1 # This specifies the measurement model for meaning_t1 meaning_t2 =~ 1*meaning_par1_t2 + c("lamb2", "lamb2")*meaning_par2_t2 + c("lamb3", "lamb3")*meaning_par3_t2 # This specifies the measurement model for meaning_t2 with the equality constrained factor loadingsmeaning_t2 ~ 1*meaning_t1 # This parameter regresses meaning_t2 perfectly on meaning_t1d_meaning_1 =~ 1*meaning_t2 # This defines the latent change score factor as measured perfectly by scores on meaning_t2meaning_t2 ~ 0*1 # This line constrains the intercept of meaning_t2 to 0meaning_t2 ~~ 0*meaning_t2 # This fixes the variance of meaning_t2 to 0d_meaning_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score meaning_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of meaning_t1 d_meaning_1 ~~ c("d_var_a", "d_var_b")*d_meaning_1 # This estimates the variance of the change scores meaning_t1 ~~ c("wb_var_a", "wb_var_b")*meaning_t1 # This estimates the variance of the meaning_t1 d_meaning_1 ~ c("fb_a", "fb_b")*meaning_t1 # This estimates the self-feedback parametermeaning_par1_t1 ~~ c("cov1a", "cov1b")*meaning_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2meaning_par2_t1 ~~ c("cov2a", "cov2b")*meaning_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2meaning_par3_t1 ~~ c("cov3a", "cov3b")*meaning_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2meaning_par1_t1 ~~ c("res1a", "res1b")*meaning_par1_t1 # This allows residual variance on indicator X1 at T1 meaning_par2_t1 ~~ c("res2a", "res2b")*meaning_par2_t1 # This allows residual variance on indicator X2 at T1meaning_par3_t1 ~~ c("res3a", "res3b")*meaning_par3_t1 # This allows residual variance on indicator X3 at T1meaning_par1_t2 ~~ c("res1a", "res1b")*meaning_par1_t2 # This allows residual variance on indicator X1 at T2 meaning_par2_t2 ~~ c("res2a", "res2b")*meaning_par2_t2 # This allows residual variance on indicator X2 at T2 meaning_par3_t2 ~~ c("res3a", "res3b")*meaning_par3_t2 # This allows residual variance on indicator X3 at T2meaning_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1meaning_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1meaning_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2meaning_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2meaning_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2'lcs_meaning_group_weak <-sem(mi_lcs_meaning_group_weak, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando", group.equal ="loadings")# strong invariancemi_lcs_meaning_group_strong <-'meaning_t1 =~ 1*meaning_par1_t1 + c("lamb2", "lamb2")*meaning_par2_t1 + c("lamb3", "lamb3")*meaning_par3_t1 # This specifies the measurement model for meaning_t1 meaning_t2 =~ 1*meaning_par1_t2 + c("lamb2", "lamb2")*meaning_par2_t2 + c("lamb3", "lamb3")*meaning_par3_t2 # This specifies the measurement model for meaning_t2 with the equality constrained factor loadingsmeaning_t2 ~ 1*meaning_t1 # This parameter regresses meaning_t2 perfectly on meaning_t1d_meaning_1 =~ 1*meaning_t2 # This defines the latent change score factor as measured perfectly by scores on meaning_t2meaning_t2 ~ 0*1 # This line constrains the intercept of meaning_t2 to 0meaning_t2 ~~ 0*meaning_t2 # This fixes the variance of meaning_t2 to 0d_meaning_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score meaning_t1 ~ c("wb_int", "wb_int")*1 # This estimates the intercept of meaning_t1 d_meaning_1 ~~ c("d_var_a", "d_var_b")*d_meaning_1 # This estimates the variance of the change scores meaning_t1 ~~ c("wb_var_a", "wb_var_b")*meaning_t1 # This estimates the variance of the meaning_t1 d_meaning_1 ~ c("fb_a", "fb_b")*meaning_t1 # This estimates the self-feedback parametermeaning_par1_t1 ~~ c("cov1a", "cov1b")*meaning_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2meaning_par2_t1 ~~ c("cov2a", "cov2b")*meaning_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2meaning_par3_t1 ~~ c("cov3a", "cov3b")*meaning_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2meaning_par1_t1 ~~ c("res1a", "res1b")*meaning_par1_t1 # This allows residual variance on indicator X1 at T1 meaning_par2_t1 ~~ c("res2a", "res2b")*meaning_par2_t1 # This allows residual variance on indicator X2 at T1meaning_par3_t1 ~~ c("res3a", "res3b")*meaning_par3_t1 # This allows residual variance on indicator X3 at T1meaning_par1_t2 ~~ c("res1a", "res1b")*meaning_par1_t2 # This allows residual variance on indicator X1 at T2 meaning_par2_t2 ~~ c("res2a", "res2b")*meaning_par2_t2 # This allows residual variance on indicator X2 at T2 meaning_par3_t2 ~~ c("res3a", "res3b")*meaning_par3_t2 # This allows residual variance on indicator X3 at T2meaning_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1meaning_par2_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1meaning_par3_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1meaning_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2meaning_par2_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2meaning_par3_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2'lcs_meaning_group_strong <-sem(mi_lcs_meaning_group_strong, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando", group.equal =c("intercepts", "loadings"))
Meaning in life: results
# model comparison tests for measurement invariancelavTestLRT(lcs_meaning_group_config, lcs_meaning_group_weak, lcs_meaning_group_strong)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
lcs_meaning_group_config 24 10472 10609 38.067
lcs_meaning_group_weak 26 10470 10598 40.184 1.9319 2 0.3806
lcs_meaning_group_strong 30 10466 10575 44.216 3.7939 4 0.4346
# show model with varying latent change parameters # -> key parameter is "d_meaning_1 ~1"# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)kable(broom::tidy(lcs_meaning_group_weak, conf.int =TRUE, conf.level =0.95) %>%select(term, label, estimate, std.all, statistic, p.value) %>%filter(term %in%c("d_meaning_1 ~1 ")), digits =3)
term
label
estimate
std.all
statistic
p.value
d_meaning_1 ~1
d_int_a
1.289
1.595
6.828
0
d_meaning_1 ~1
d_int_b
1.479
1.792
7.535
0
# constrained to be equal in the strong measurement invariance model:kable(broom::tidy(lcs_meaning_group_strong, conf.int =TRUE, conf.level =0.95) %>%select(term, label, estimate, std.all, statistic, p.value) %>%filter(term %in%c("d_meaning_1 ~1 ")), digits =3)
term
label
estimate
std.all
statistic
p.value
d_meaning_1 ~1
d_int
1.384
1.682
10.052
0
d_meaning_1 ~1
d_int
1.384
1.701
10.052
0
Slightly more positive change in meaning in life in the Self-Improvement group but no substantial differences according to the LRTs.
# whole model (weak invariance)summary(lcs_meaning_group_weak, fit.measures=TRUE, standardized=TRUE, rsquare=F)
# adapt latent change score model from above and add grouping factor in estimation (also add vectorized equality constraints to the model)# configural invariancemi_lcs_selfes_group_config <-'selfes_t1 =~ 1*selfes_par1_t1 + c("lamb2a", "lamb2b")*selfes_par2_t1 + c("lamb3a", "lamb3b")*selfes_par3_t1 # This specifies the measurement model for selfes_t1 selfes_t2 =~ 1*selfes_par1_t2 + c("lamb2a", "lamb2b")*selfes_par2_t2 + c("lamb3a", "lamb3b")*selfes_par3_t2 # This specifies the measurement model for selfes_t2 with the equality constrained factor loadingsselfes_t2 ~ 1*selfes_t1 # This parameter regresses selfes_t2 perfectly on selfes_t1d_selfes_1 =~ 1*selfes_t2 # This defines the latent change score factor as measured perfectly by scores on selfes_t2selfes_t2 ~ 0*1 # This line constrains the intercept of selfes_t2 to 0selfes_t2 ~~ 0*selfes_t2 # This fixes the variance of selfes_t2 to 0d_selfes_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score selfes_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of selfes_t1 d_selfes_1 ~~ c("d_var_a", "d_var_b")*d_selfes_1 # This estimates the variance of the change scores selfes_t1 ~~ c("wb_var_a", "wb_var_b")*selfes_t1 # This estimates the variance of the selfes_t1 d_selfes_1 ~ c("fb_a", "fb_b")*selfes_t1 # This estimates the self-feedback parameterselfes_par1_t1 ~~ c("cov1a", "cov1b")*selfes_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2selfes_par2_t1 ~~ c("cov2a", "cov2b")*selfes_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2selfes_par3_t1 ~~ c("cov3a", "cov3b")*selfes_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2selfes_par1_t1 ~~ c("res1a", "res1b")*selfes_par1_t1 # This allows residual variance on indicator X1 at T1 selfes_par2_t1 ~~ c("res2a", "res2b")*selfes_par2_t1 # This allows residual variance on indicator X2 at T1selfes_par3_t1 ~~ c("res3a", "res3b")*selfes_par3_t1 # This allows residual variance on indicator X3 at T1selfes_par1_t2 ~~ c("res1a", "res1b")*selfes_par1_t2 # This allows residual variance on indicator X1 at T2 selfes_par2_t2 ~~ c("res2a", "res2b")*selfes_par2_t2 # This allows residual variance on indicator X2 at T2 selfes_par3_t2 ~~ c("res3a", "res3b")*selfes_par3_t2 # This allows residual variance on indicator X3 at T2selfes_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1selfes_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1selfes_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2selfes_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2selfes_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2'lcs_selfes_group_config <-sem(mi_lcs_selfes_group_config, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando")# weak invariancemi_lcs_selfes_group_weak <-'selfes_t1 =~ 1*selfes_par1_t1 + c("lamb2", "lamb2")*selfes_par2_t1 + c("lamb3", "lamb3")*selfes_par3_t1 # This specifies the measurement model for selfes_t1 selfes_t2 =~ 1*selfes_par1_t2 + c("lamb2", "lamb2")*selfes_par2_t2 + c("lamb3", "lamb3")*selfes_par3_t2 # This specifies the measurement model for selfes_t2 with the equality constrained factor loadingsselfes_t2 ~ 1*selfes_t1 # This parameter regresses selfes_t2 perfectly on selfes_t1d_selfes_1 =~ 1*selfes_t2 # This defines the latent change score factor as measured perfectly by scores on selfes_t2selfes_t2 ~ 0*1 # This line constrains the intercept of selfes_t2 to 0selfes_t2 ~~ 0*selfes_t2 # This fixes the variance of selfes_t2 to 0d_selfes_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score selfes_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of selfes_t1 d_selfes_1 ~~ c("d_var_a", "d_var_b")*d_selfes_1 # This estimates the variance of the change scores selfes_t1 ~~ c("wb_var_a", "wb_var_b")*selfes_t1 # This estimates the variance of the selfes_t1 d_selfes_1 ~ c("fb_a", "fb_b")*selfes_t1 # This estimates the self-feedback parameterselfes_par1_t1 ~~ c("cov1a", "cov1b")*selfes_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2selfes_par2_t1 ~~ c("cov2a", "cov2b")*selfes_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2selfes_par3_t1 ~~ c("cov3a", "cov3b")*selfes_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2selfes_par1_t1 ~~ c("res1a", "res1b")*selfes_par1_t1 # This allows residual variance on indicator X1 at T1 selfes_par2_t1 ~~ c("res2a", "res2b")*selfes_par2_t1 # This allows residual variance on indicator X2 at T1selfes_par3_t1 ~~ c("res3a", "res3b")*selfes_par3_t1 # This allows residual variance on indicator X3 at T1selfes_par1_t2 ~~ c("res1a", "res1b")*selfes_par1_t2 # This allows residual variance on indicator X1 at T2 selfes_par2_t2 ~~ c("res2a", "res2b")*selfes_par2_t2 # This allows residual variance on indicator X2 at T2 selfes_par3_t2 ~~ c("res3a", "res3b")*selfes_par3_t2 # This allows residual variance on indicator X3 at T2selfes_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1selfes_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1selfes_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2selfes_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2selfes_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2'lcs_selfes_group_weak <-sem(mi_lcs_selfes_group_weak, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando", group.equal ="loadings")# strong invariancemi_lcs_selfes_group_strong <-'selfes_t1 =~ 1*selfes_par1_t1 + c("lamb2", "lamb2")*selfes_par2_t1 + c("lamb3", "lamb3")*selfes_par3_t1 # This specifies the measurement model for selfes_t1 selfes_t2 =~ 1*selfes_par1_t2 + c("lamb2", "lamb2")*selfes_par2_t2 + c("lamb3", "lamb3")*selfes_par3_t2 # This specifies the measurement model for selfes_t2 with the equality constrained factor loadingsselfes_t2 ~ 1*selfes_t1 # This parameter regresses selfes_t2 perfectly on selfes_t1d_selfes_1 =~ 1*selfes_t2 # This defines the latent change score factor as measured perfectly by scores on selfes_t2selfes_t2 ~ 0*1 # This line constrains the intercept of selfes_t2 to 0selfes_t2 ~~ 0*selfes_t2 # This fixes the variance of selfes_t2 to 0d_selfes_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score selfes_t1 ~ c("wb_int", "wb_int")*1 # This estimates the intercept of selfes_t1 d_selfes_1 ~~ c("d_var_a", "d_var_b")*d_selfes_1 # This estimates the variance of the change scores selfes_t1 ~~ c("wb_var_a", "wb_var_b")*selfes_t1 # This estimates the variance of the selfes_t1 d_selfes_1 ~ c("fb_a", "fb_b")*selfes_t1 # This estimates the self-feedback parameterselfes_par1_t1 ~~ c("cov1a", "cov1b")*selfes_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2selfes_par2_t1 ~~ c("cov2a", "cov2b")*selfes_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2selfes_par3_t1 ~~ c("cov3a", "cov3b")*selfes_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2selfes_par1_t1 ~~ c("res1a", "res1b")*selfes_par1_t1 # This allows residual variance on indicator X1 at T1 selfes_par2_t1 ~~ c("res2a", "res2b")*selfes_par2_t1 # This allows residual variance on indicator X2 at T1selfes_par3_t1 ~~ c("res3a", "res3b")*selfes_par3_t1 # This allows residual variance on indicator X3 at T1selfes_par1_t2 ~~ c("res1a", "res1b")*selfes_par1_t2 # This allows residual variance on indicator X1 at T2 selfes_par2_t2 ~~ c("res2a", "res2b")*selfes_par2_t2 # This allows residual variance on indicator X2 at T2 selfes_par3_t2 ~~ c("res3a", "res3b")*selfes_par3_t2 # This allows residual variance on indicator X3 at T2selfes_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1selfes_par2_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1selfes_par3_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1selfes_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2selfes_par2_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2selfes_par3_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2'lcs_selfes_group_strong <-sem(mi_lcs_selfes_group_strong, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando", group.equal =c("intercepts", "loadings"))
Meaning in life: results
# model comparison tests for measurement invariancelavTestLRT(lcs_selfes_group_config, lcs_selfes_group_weak, lcs_selfes_group_strong)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
lcs_selfes_group_config 24 7347.4 7484.5 20.707
lcs_selfes_group_weak 26 7345.5 7473.4 22.790 2.0845 2 0.3527
lcs_selfes_group_strong 30 7344.2 7453.9 29.500 6.4175 4 0.1701
# show model with varying latent change parameters # -> key parameter is "d_selfes_1 ~1"# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)kable(broom::tidy(lcs_selfes_group_weak, conf.int =TRUE, conf.level =0.95) %>%select(term, label, estimate, std.all, statistic, p.value) %>%filter(term %in%c("d_selfes_1 ~1 ")), digits =3)
term
label
estimate
std.all
statistic
p.value
d_selfes_1 ~1
d_int_a
1.178
2.061
7.605
0
d_selfes_1 ~1
d_int_b
0.965
1.846
6.713
0
# constrained to be equal in the strong measurement invariance model:kable(broom::tidy(lcs_selfes_group_strong, conf.int =TRUE, conf.level =0.95) %>%select(term, label, estimate, std.all, statistic, p.value) %>%filter(term %in%c("d_selfes_1 ~1 ")), digits =3)
term
label
estimate
std.all
statistic
p.value
d_selfes_1 ~1
d_int
1.077
1.916
10.19
0
d_selfes_1 ~1
d_int
1.077
2.020
10.19
0
Slightly more positive change in self-esteem in the Self-Acceptance group but no substantial differences according to the LRTs.
# whole model (weak invariance)summary(lcs_selfes_group_weak, fit.measures=TRUE, standardized=TRUE, rsquare=F)
# adapt latent change score model from above and add grouping factor in estimation (also add vectorized equality constraints to the model)# configural invariancemi_lcs_concept_group_config <-'concept_t1 =~ 1*concept_par1_t1 + c("lamb2a", "lamb2b")*concept_par2_t1 + c("lamb3a", "lamb3b")*concept_par3_t1 # This specifies the measurement model for concept_t1 concept_t2 =~ 1*concept_par1_t2 + c("lamb2a", "lamb2b")*concept_par2_t2 + c("lamb3a", "lamb3b")*concept_par3_t2 # This specifies the measurement model for concept_t2 with the equality constrained factor loadingsconcept_t2 ~ 1*concept_t1 # This parameter regresses concept_t2 perfectly on concept_t1d_concept_1 =~ 1*concept_t2 # This defines the latent change score factor as measured perfectly by scores on concept_t2concept_t2 ~ 0*1 # This line constrains the intercept of concept_t2 to 0concept_t2 ~~ 0*concept_t2 # This fixes the variance of concept_t2 to 0d_concept_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score concept_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of concept_t1 d_concept_1 ~~ c("d_var_a", "d_var_b")*d_concept_1 # This estimates the variance of the change scores concept_t1 ~~ c("wb_var_a", "wb_var_b")*concept_t1 # This estimates the variance of the concept_t1 d_concept_1 ~ c("fb_a", "fb_b")*concept_t1 # This estimates the self-feedback parameterconcept_par1_t1 ~~ c("cov1a", "cov1b")*concept_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2concept_par2_t1 ~~ c("cov2a", "cov2b")*concept_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2concept_par3_t1 ~~ c("cov3a", "cov3b")*concept_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2concept_par1_t1 ~~ c("res1a", "res1b")*concept_par1_t1 # This allows residual variance on indicator X1 at T1 concept_par2_t1 ~~ c("res2a", "res2b")*concept_par2_t1 # This allows residual variance on indicator X2 at T1concept_par3_t1 ~~ c("res3a", "res3b")*concept_par3_t1 # This allows residual variance on indicator X3 at T1concept_par1_t2 ~~ c("res1a", "res1b")*concept_par1_t2 # This allows residual variance on indicator X1 at T2 concept_par2_t2 ~~ c("res2a", "res2b")*concept_par2_t2 # This allows residual variance on indicator X2 at T2 concept_par3_t2 ~~ c("res3a", "res3b")*concept_par3_t2 # This allows residual variance on indicator X3 at T2concept_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1concept_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1concept_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2concept_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2concept_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2'lcs_concept_group_config <-sem(mi_lcs_concept_group_config, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando")# weak invariancemi_lcs_concept_group_weak <-'concept_t1 =~ 1*concept_par1_t1 + c("lamb2", "lamb2")*concept_par2_t1 + c("lamb3", "lamb3")*concept_par3_t1 # This specifies the measurement model for concept_t1 concept_t2 =~ 1*concept_par1_t2 + c("lamb2", "lamb2")*concept_par2_t2 + c("lamb3", "lamb3")*concept_par3_t2 # This specifies the measurement model for concept_t2 with the equality constrained factor loadingsconcept_t2 ~ 1*concept_t1 # This parameter regresses concept_t2 perfectly on concept_t1d_concept_1 =~ 1*concept_t2 # This defines the latent change score factor as measured perfectly by scores on concept_t2concept_t2 ~ 0*1 # This line constrains the intercept of concept_t2 to 0concept_t2 ~~ 0*concept_t2 # This fixes the variance of concept_t2 to 0d_concept_1 ~ c("d_int_a", "d_int_b")*1 # This estimates the intercept of the change score concept_t1 ~ c("wb_int_a", "wb_int_b")*1 # This estimates the intercept of concept_t1 d_concept_1 ~~ c("d_var_a", "d_var_b")*d_concept_1 # This estimates the variance of the change scores concept_t1 ~~ c("wb_var_a", "wb_var_b")*concept_t1 # This estimates the variance of the concept_t1 d_concept_1 ~ c("fb_a", "fb_b")*concept_t1 # This estimates the self-feedback parameterconcept_par1_t1 ~~ c("cov1a", "cov1b")*concept_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2concept_par2_t1 ~~ c("cov2a", "cov2b")*concept_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2concept_par3_t1 ~~ c("cov3a", "cov3b")*concept_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2concept_par1_t1 ~~ c("res1a", "res1b")*concept_par1_t1 # This allows residual variance on indicator X1 at T1 concept_par2_t1 ~~ c("res2a", "res2b")*concept_par2_t1 # This allows residual variance on indicator X2 at T1concept_par3_t1 ~~ c("res3a", "res3b")*concept_par3_t1 # This allows residual variance on indicator X3 at T1concept_par1_t2 ~~ c("res1a", "res1b")*concept_par1_t2 # This allows residual variance on indicator X1 at T2 concept_par2_t2 ~~ c("res2a", "res2b")*concept_par2_t2 # This allows residual variance on indicator X2 at T2 concept_par3_t2 ~~ c("res3a", "res3b")*concept_par3_t2 # This allows residual variance on indicator X3 at T2concept_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1concept_par2_t1 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T1concept_par3_t1 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T1concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2concept_par2_t2 ~ c("m2a", "m2b")*1 # This estimates the intercept of X2 at T2concept_par3_t2 ~ c("m3a", "m3b")*1 # This estimates the intercept of X3 at T2'lcs_concept_group_weak <-sem(mi_lcs_concept_group_weak, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando", group.equal ="loadings")# strong invariancemi_lcs_concept_group_strong <-'concept_t1 =~ 1*concept_par1_t1 + c("lamb2", "lamb2")*concept_par2_t1 + c("lamb3", "lamb3")*concept_par3_t1 # This specifies the measurement model for concept_t1 concept_t2 =~ 1*concept_par1_t2 + c("lamb2", "lamb2")*concept_par2_t2 + c("lamb3", "lamb3")*concept_par3_t2 # This specifies the measurement model for concept_t2 with the equality constrained factor loadingsconcept_t2 ~ 1*concept_t1 # This parameter regresses concept_t2 perfectly on concept_t1d_concept_1 =~ 1*concept_t2 # This defines the latent change score factor as measured perfectly by scores on concept_t2concept_t2 ~ 0*1 # This line constrains the intercept of concept_t2 to 0concept_t2 ~~ 0*concept_t2 # This fixes the variance of concept_t2 to 0d_concept_1 ~ c("d_int", "d_int")*1 # This estimates the intercept of the change score concept_t1 ~ c("wb_int", "wb_int")*1 # This estimates the intercept of concept_t1 d_concept_1 ~~ c("d_var_a", "d_var_b")*d_concept_1 # This estimates the variance of the change scores concept_t1 ~~ c("wb_var_a", "wb_var_b")*concept_t1 # This estimates the variance of the concept_t1 d_concept_1 ~ c("fb_a", "fb_b")*concept_t1 # This estimates the self-feedback parameterconcept_par1_t1 ~~ c("cov1a", "cov1b")*concept_par1_t2 # This allows residual covariance on indicator X1 across T1 and T2concept_par2_t1 ~~ c("cov2a", "cov2b")*concept_par2_t2 # This allows residual covariance on indicator X2 across T1 and T2concept_par3_t1 ~~ c("cov3a", "cov3b")*concept_par3_t2 # This allows residual covariance on indicator X3 across T1 and T2concept_par1_t1 ~~ c("res1a", "res1b")*concept_par1_t1 # This allows residual variance on indicator X1 at T1 concept_par2_t1 ~~ c("res2a", "res2b")*concept_par2_t1 # This allows residual variance on indicator X2 at T1concept_par3_t1 ~~ c("res3a", "res3b")*concept_par3_t1 # This allows residual variance on indicator X3 at T1concept_par1_t2 ~~ c("res1a", "res1b")*concept_par1_t2 # This allows residual variance on indicator X1 at T2 concept_par2_t2 ~~ c("res2a", "res2b")*concept_par2_t2 # This allows residual variance on indicator X2 at T2 concept_par3_t2 ~~ c("res3a", "res3b")*concept_par3_t2 # This allows residual variance on indicator X3 at T2concept_par1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1concept_par2_t1 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T1concept_par3_t1 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T1concept_par1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2concept_par2_t2 ~ c("m2", "m2")*1 # This estimates the intercept of X2 at T2concept_par3_t2 ~ c("m3", "m3")*1 # This estimates the intercept of X3 at T2'lcs_concept_group_strong <-sem(mi_lcs_concept_group_strong, data=df_sbsa_wide_wb, estimator='mlr', fixed.x=FALSE, missing='fiml', group ="rando", group.equal =c("intercepts", "loadings"))
Meaning in life: results
# model comparison tests for measurement invariancelavTestLRT(lcs_concept_group_config, lcs_concept_group_weak, lcs_concept_group_strong)
Scaled Chi-Squared Difference Test (method = "satorra.bentler.2001")
lavaan NOTE:
The "Chisq" column contains standard test statistics, not the
robust test that should be reported per model. A robust difference
test is a function of two standard (not robust) statistics.
Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
lcs_concept_group_config 24 8376.4 8513.5 32.823
lcs_concept_group_weak 26 8373.0 8500.9 33.355 0.46999 2 0.7906
lcs_concept_group_strong 30 8365.7 8475.4 34.073 0.72313 4 0.9484
# show model with varying latent change parameters # -> key parameter is "d_concept_1 ~1"# labelled parameter as "d_int_a" & "d_int_b" (a = Self-Acceptance group, b = Self-Improvement group)kable(broom::tidy(lcs_concept_group_weak, conf.int =TRUE, conf.level =0.95) %>%select(term, label, estimate, std.all, statistic, p.value) %>%filter(term %in%c("d_concept_1 ~1 ")), digits =3)
term
label
estimate
std.all
statistic
p.value
d_concept_1 ~1
d_int_a
1.234
2.125
7.949
0
d_concept_1 ~1
d_int_b
1.077
1.986
7.104
0
# constrained to be equal in the strong measurement invariance model:kable(broom::tidy(lcs_concept_group_strong, conf.int =TRUE, conf.level =0.95) %>%select(term, label, estimate, std.all, statistic, p.value) %>%filter(term %in%c("d_concept_1 ~1 ")), digits =3)
term
label
estimate
std.all
statistic
p.value
d_concept_1 ~1
d_int
1.159
2.024
10.612
0
d_concept_1 ~1
d_int
1.159
2.108
10.612
0
Slightly more positive change in self-concept clarity in the Self-Acceptance group but no substantial differences according to the LRTs.
# whole model (weak invariance)summary(lcs_concept_group_weak, fit.measures=TRUE, standardized=TRUE, rsquare=F)
7.1.2 Current- and ideal-self personality differences across groups
Profile correlations by group and measurement occasion (mixed effects models) - results:
df_sbsa <- df_sbsa %>%mutate(time_d = time -1)psych::describeBy(df_sbsa$profile_corr_item_z, list(df_sbsa$rando, df_sbsa$time_d))
Descriptive statistics by group
: Self-Acceptance
: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 342 0.36 0.48 0.36 0.35 0.47 -1.05 2.25 3.3 0.39 0.99 0.03
------------------------------------------------------------
: Self-Improvement
: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 362 0.38 0.45 0.36 0.35 0.44 -0.71 2.01 2.72 0.59 0.68 0.02
------------------------------------------------------------
: Self-Acceptance
: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 326 0.55 0.58 0.49 0.51 0.52 -0.9 3.32 4.22 1 3.31 0.03
------------------------------------------------------------
: Self-Improvement
: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 338 0.56 0.48 0.53 0.55 0.48 -0.77 2.27 3.04 0.19 0.25 0.03
Descriptive statistics by group
: Self-Acceptance
: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 342 0.27 0.67 0.21 0.25 0.64 -1.5 2.67 4.17 0.46 0.68 0.04
------------------------------------------------------------
: Self-Improvement
: 0
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 362 0.29 0.63 0.23 0.25 0.65 -1.06 2.44 3.5 0.56 0.27 0.03
------------------------------------------------------------
: Self-Acceptance
: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 326 0.49 0.76 0.45 0.46 0.72 -1.17 3.73 4.9 0.69 1.5 0.04
------------------------------------------------------------
: Self-Improvement
: 1
vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 338 0.52 0.69 0.5 0.5 0.74 -1.22 2.54 3.77 0.17 -0.31 0.04
With both conceptualizations of the similarity / difference of current- and ideal-level personality (profile correlations / squared differences), we find no significant effects of group (at T1; with few exceptions…) or of interaction effects of group by measurement occasion (at T2).
7.2 Personal project dimensions (b)
We will explore b) whether the extent of change/acceptance is related to personal project dimension variables.
For now, I use the personal project dimension variables assessed at T1:
Skill-building group: “How important is it for you to change your personality?”
Skill-building group: “How difficult is it for you to work on changing your personality?”
Self-acceptance group: “How important is it for you to accept your personality?”
Self-acceptance group: “How difficult is it for you to work on accepting your personality?”
They were, however, also assessed at T2:
Skill-building group: “During this study, how important was it for you to change your personality?”
Skill-building group: “During this study, how difficult was it for you to work on changing your personality?”
Self-acceptance group: “During this study, how important was it for you to accept your personality?”
Self-acceptance group: “During this study, how difficult was it for you to work on accepting your personality?”
7.2.1 Personal project dimensions as moderators of change in personality in self-improvement group
Reshape and split data set by intervention group:
Show the code
df_sbsa_wide_pers_sb_ppd <- df_sbsa %>%filter(rando=="Self-Improvement") %>%arrange(pid, time) %>%select(pid, time, starts_with(c("sb01"))) %>%# Personal project dimensions - self improvementpivot_wider(names_from = time,names_sep ="_t",values_from =c(starts_with(c("sb01")))) %>%select(-c(sb01_01_t2, sb01_02_t2))# colnames(df_sbsa_wide_pers_sb_ppd)group_assign <- df_sbsa %>%select(pid, rando) %>%unique()df_sbsa_wide_pers_sb_ppd <- df_sbsa_wide_pers %>%left_join(group_assign) %>%filter(rando=="Self-Improvement") %>%select(-rando) %>%left_join(df_sbsa_wide_pers_sb_ppd)# need to form a mean score because some models did not converge when using the latent factor of PPD (but high correlation between the two items)df_sbsa_wide_pers_sb_ppd <- df_sbsa_wide_pers_sb_ppd %>%mutate(ppd =rowMeans(across(c(sb01_01_t1, sb01_02_t1)), na.rm=T))
7.2.1.1 Big Five traits
Run models for all traits with a template & loop:
Show the code
# create templates:# 1st, for facet-specific change goalstrait_template_ppd_improve <-'trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for trait_t1 trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadingstrait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0d_trait_1 ~ 1 # This estimates the intercept of the change score trait_t1 ~ 1 # This estimates the intercept of trait_t1 d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1 trait_t1 ~ ppd # This estimates the moderation effect on personality at T1d_trait_1 ~ trait_t1 + ppd # This estimates the self-feedback parameter and the moderation effect on the change scoreind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1 ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2 ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2 ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2ppd ~~ ppdppd ~ 1'# loop across 5 traitsfor (i in1:5) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post current# items = paste0(bfi_versions[[5]], item_nrs) # using parcels instead! template_filled <-str_replace_all(trait_template_ppd_improve, c("trait"= short_name,"ind01"=paste0(short_name, "_curr_par1"), "ind02"=paste0(short_name, "_curr_par2"), "ind03"=paste0(short_name, "_curr_par3"))) trait_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sb_ppd, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_ppd")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_ppd")), trait_model_fit))}
7.2.1.1.1 Extraversion: personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the agreeableness change score (current-self) is not significantly different from zero, b = -0.009, p = 0.624.
7.2.1.1.3 Conscientiousness: personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the conscientiousness change score (current-self) is not significantly different from zero, b = -0.01, p = 0.689.
7.2.1.1.4 Neuroticism: personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the neuroticism change score (current-self) is not significantly different from zero, b = -0.028, p = 0.376.
7.2.1.1.5 Openness: personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the openness change score (current-self) is not significantly different from zero, b = -0.003, p = 0.879.
7.2.1.2 Big Five facets
Run models for all facets with a template & loop:
Show the code
# create templates:facet_template_ppd_improve <-'facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0d_facet_1 ~ 1 # This estimates the intercept of the change score facet_t1 ~ 1 # This estimates the intercept of facet_t1 d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1 facet_t1 ~ ppd # This estimates the moderation effect on personality at T1d_facet_1 ~ facet_t1 + ppd # This estimates the self-feedback parameter and the moderation effect on the change scoreind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1 ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2 ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2 ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2ppd ~~ ppdppd ~ 1'# loop across 15 facetsfor (i in6:length(b5_vars)) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post current items =paste0(bfi_versions[[5]], item_nrs) template_filled <-str_replace_all(facet_template_ppd_improve, c("facet"= short_name,"ind1"= items[1], "ind2"= items[2], "ind3"= items[3], "ind4"= items[4])) facet_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sb_ppd, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_ppd")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[5], 6), "_ppd")), facet_model_fit))}
7.2.1.2.1 Sociability - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the sociability change score (current-self) is not significantly different from zero, b = 0.08, p = 0.081.
7.2.1.2.2 Assertiveness - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the energy change score (current-self) is not significantly different from zero, b = -0.019, p = 0.394.
7.2.1.2.4 Compassion - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the compassion change score (current-self) is not significantly different from zero, b = 0.041, p = 0.205.
7.2.1.2.5 Respectfulness - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the respectfulness change score (current-self) is not significantly different from zero, b = -0.033, p = 0.176.
7.2.1.2.6 Trust - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the trust change score (current-self) is not significantly different from zero, b = 0.01, p = 0.756.
7.2.1.2.7 Organization - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the organization change score (current-self) is not significantly different from zero, b = -0.039, p = 0.312.
7.2.1.2.8 Productiveness - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the productiveness change score (current-self) is not significantly different from zero, b = 0, p = 0.999.
7.2.1.2.9 Responsibility - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the anxiety change score (current-self) is not significantly different from zero, b = 0.043, p = 0.302.
7.2.1.2.11 Depression - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the depression change score (current-self) is not significantly different from zero, b = 0.032, p = 0.276.
7.2.1.2.12 Volatility - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the volatility change score (current-self) is not significantly different from zero, b = 0.003, p = 0.941.
7.2.1.2.13 Curiosity - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the curiosity change score (current-self) is not significantly different from zero, b = -0.014, p = 0.613.
7.2.1.2.14 Aesthetic - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
(here there were some convergence problems with the standard model that the loop tried to fit)
mi_lcs_aesth_curr_ppd <-'aesth_t1 =~ 1*bf05_05_t1 + lamb2*bf05_20_t1 + lamb3*bf05_35_t1 + lamb4*bf05_50_t1 # This specifies the measurement model for aesth at T1aesth_t2 =~ 1*bf05_05_t2 + lamb2*bf05_20_t2 + lamb3*bf05_35_t2 + lamb4*bf05_50_t2 # This specifies the measurement model for aesth at T2 (with equality constraints)aesth_t2 ~ 1*aesth_t1 # This parameter regresses aesth_t2 perfectly on aesth_t1d_aesth_1 =~ 1*aesth_t2 # This defines the latent change score factor as measured perfectly by scores on aesth_t2aesth_t2 ~ 0*1 # This line constrains the intercept of aesth_t2 to 0aesth_t2 ~~ 0*aesth_t2 # This fixes the variance of aesth_t2 to 0d_aesth_1 ~ 1 # This estimates the intercept of the change score aesth_t1 ~ 1 # This estimates the intercept of aesth_t1 d_aesth_1 ~~ d_aesth_1 # This estimates the variance of the change scores aesth_t1 ~~ aesth_t1 # This estimates the variance of aesth_t1 aesth_t1 ~ ppd # This estimates the moderation effect on personality at T1d_aesth_1 ~ aesth_t1 + ppd # This estimates the self-feedback parameter and the moderation effect on the change scorebf05_05_t1 ~~ bf05_05_t2 # This allows residual covariance on indicator X1 across T1 and T2bf05_20_t1 ~~ bf05_20_t2 # This allows residual covariance on indicator X2 across T1 and T2bf05_35_t1 ~~ bf05_35_t2 # This allows residual covariance on indicator X3 across T1 and T2bf05_50_t1 ~~ bf05_50_t2 # This allows residual covariance on indicator X4 across T1 and T2bf05_05_t1 ~~ res1*bf05_05_t1 # This allows residual variance on indicator X1 at T1 bf05_20_t1 ~~ res2*bf05_20_t1 # This allows residual variance on indicator X2 at T1bf05_35_t1 ~~ res3*bf05_35_t1 # This allows residual variance on indicator X3 at T1bf05_50_t1 ~~ res4*bf05_50_t1 # This allows residual variance on indicator X4 at T1bf05_05_t2 ~~ res1*bf05_05_t2 # This allows residual variance on indicator X1 at T2 bf05_20_t2 ~~ res2*bf05_20_t2 # This allows residual variance on indicator X2 at T2 bf05_35_t2 ~~ res3*bf05_35_t2 # This allows residual variance on indicator X3 at T2bf05_50_t2 ~~ res4*bf05_50_t2 # This allows residual variance on indicator X4 at T2bf05_05_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1bf05_20_t1 ~ m2*1 # This estimates the intercept of X2 at T1bf05_35_t1 ~ m3*1 # This estimates the intercept of X3 at T1bf05_50_t1 ~ m4*1 # This estimates the intercept of X4 at T1bf05_05_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2bf05_20_t2 ~ m2*1 # This estimates the intercept of X2 at T2bf05_35_t2 ~ m3*1 # This estimates the intercept of X3 at T2bf05_50_t2 ~ m4*1 # This estimates the intercept of X4 at T2ppd ~~ ppdppd ~ 1'fit_mi_lcs_aesth_curr_ppd <-lavaan(mi_lcs_aesth_curr_ppd, data=df_sbsa_wide_pers_sb_ppd %>%filter(!is.na(bf05_05_t1) &!is.na(bf05_05_t2)), estimator='mlr', fixed.x=FALSE, missing="fiml")# This model did not converge properly with missing data and FIML -> no problem when only using complete datasummary(fit_mi_lcs_aesth_curr_ppd, fit.measures=TRUE, standardized=TRUE, rsquare=F)
The moderation effect of personal project dimensions with the aesthetic change score (current-self) is not significantly different from zero, b = -0.002, p = 0.268.
7.2.1.2.15 Imagination - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the imagination change score (current-self) is not significantly different from zero, b = 0.03, p = 0.38.
Results summary across the Big Five traits: personal project dimensions (ppd) as moderators on the latent change score
kable(df_table_ppd_improve[1:5, ], digits =3)
trait
moderator
estimate
std.all
statistic
p.value
extraversion
ppd
0.065
0.170
2.364
0.018
agreeableness
ppd
-0.009
-0.037
-0.490
0.624
conscientiousness
ppd
-0.010
-0.025
-0.400
0.689
neuroticism
ppd
-0.028
-0.052
-0.884
0.376
openness
ppd
-0.003
-0.014
-0.152
0.879
Only one moderator effect significantly differs from zero:
changes in current-level extraversion are moderated by the personal project dimensions
Results summary across the Big Five facets: personal project dimensions (ppd) as moderators on the latent change score
kable(df_table_ppd_improve[6:20, ], digits =3)
trait
moderator
estimate
std.all
statistic
p.value
sociability
ppd
0.080
0.141
1.743
0.081
assertiveness
ppd
0.064
0.243
2.094
0.036
energy
ppd
-0.019
-0.077
-0.852
0.394
compassion
ppd
0.041
0.151
1.267
0.205
respectfulness
ppd
-0.033
-0.093
-1.353
0.176
trust
ppd
0.010
0.027
0.311
0.756
organization
ppd
-0.039
-0.070
-1.011
0.312
productiveness
ppd
0.000
0.000
-0.002
0.999
responsibility
ppd
-0.049
-0.217
-2.068
0.039
anxiety
ppd
0.043
0.069
1.033
0.302
depression
ppd
0.032
0.081
1.089
0.276
volatility
ppd
0.003
0.005
0.075
0.941
curiosity
ppd
-0.014
-0.065
-0.506
0.613
aesthetic
ppd
-0.002
-0.089
-1.109
0.268
imagination
ppd
0.030
0.079
0.877
0.380
Two significant moderation effects of personal project dimensions on the facet-level:
- Changes in current-level assertiveness and responsibility are moderated by personal project dimensions.
7.2.2 Personal project dimensions as moderators of change in personality in self-acceptance group
Reshape and split data set by intervention group:
Show the code
df_sbsa_wide_pers_sa_ppd <- df_sbsa %>%filter(rando=="Self-Acceptance") %>%arrange(pid, time) %>%select(pid, time, starts_with(c("sa01"))) %>%pivot_wider(names_from = time,names_sep ="_t",values_from =c(starts_with(c("sa01")))) %>%select(-c(sa01_01_t2, sa01_02_t2)) # colnames(df_sbsa_wide_pers_sa_ppd)group_assign <- df_sbsa %>%select(pid, rando) %>%unique()df_sbsa_wide_pers_sa_ppd <- df_sbsa_wide_pers %>%left_join(group_assign) %>%filter(rando=="Self-Acceptance") %>%select(-rando) %>%left_join(df_sbsa_wide_pers_sa_ppd)# need to form a mean score because some models did not converge when using the latent factor of PPD (but high correlation between the two items)df_sbsa_wide_pers_sa_ppd <- df_sbsa_wide_pers_sa_ppd %>%mutate(ppd =rowMeans(across(c(sa01_01_t1, sa01_02_t1)), na.rm=T))
7.2.2.1 Big Five traits
Run models for all traits with a template & loop:
Show the code
# create templatestrait_template_ppd_accept <-'trait_t1 =~ 1*ind01_t1 + lamb2*ind02_t1 + lamb3*ind03_t1 # This specifies the measurement model for trait_t1 trait_t2 =~ 1*ind01_t2 + lamb2*ind02_t2 + lamb3*ind03_t2 # This specifies the measurement model for trait_t2 with the equality constrained factor loadingstrait_t2 ~ 1*trait_t1 # This parameter regresses trait_t2 perfectly on trait_t1d_trait_1 =~ 1*trait_t2 # This defines the latent change score factor as measured perfectly by scores on trait_t2trait_t2 ~ 0*1 # This line constrains the intercept of trait_t2 to 0trait_t2 ~~ 0*trait_t2 # This fixes the variance of trait_t2 to 0d_trait_1 ~ 1 # This estimates the intercept of the change score trait_t1 ~ 1 # This estimates the intercept of trait_t1 d_trait_1 ~~ d_trait_1 # This estimates the variance of the change scores trait_t1 ~~ trait_t1 # This estimates the variance of trait_t1 trait_t1 ~ ppd # This estimates the moderation effect on personality at T1d_trait_1 ~ trait_t1 + ppd # This estimates the self-feedback parameter and the moderation effect on the change scoreind01_t1 ~~ ind01_t2 # This allows residual covariance on indicator X1 across T1 and T2ind02_t1 ~~ ind02_t2 # This allows residual covariance on indicator X2 across T1 and T2ind03_t1 ~~ ind03_t2 # This allows residual covariance on indicator X3 across T1 and T2ind01_t1 ~~ res1*ind01_t1 # This allows residual variance on indicator X1 at T1 ind02_t1 ~~ res2*ind02_t1 # This allows residual variance on indicator X2 at T1ind03_t1 ~~ res3*ind03_t1 # This allows residual variance on indicator X3 at T1ind01_t2 ~~ res1*ind01_t2 # This allows residual variance on indicator X1 at T2 ind02_t2 ~~ res2*ind02_t2 # This allows residual variance on indicator X2 at T2 ind03_t2 ~~ res3*ind03_t2 # This allows residual variance on indicator X3 at T2ind01_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind02_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind03_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind01_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind02_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind03_t2 ~ m3*1 # This estimates the intercept of X3 at T2ppd ~~ ppdppd ~ 1'# loop across 5 traitsfor (i in1:5) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post ideal (6 = ideal)# items = paste0(bfi_versions[[6]], item_nrs) # using parcels instead! template_filled <-str_replace_all(trait_template_ppd_accept, c("trait"= short_name,"ind01"=paste0(short_name, "_ideal_par1"), "ind02"=paste0(short_name, "_ideal_par2"), "ind03"=paste0(short_name, "_ideal_par3"))) trait_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sa_ppd, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_ppd")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_ppd")), trait_model_fit))}
7.2.2.1.1 Extraversion: personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the extraversion change score (ideal-self) is not significantly different from zero, b = 0.021, p = 0.53.
7.2.2.1.2 Agreeableness: personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the agreeableness change score (ideal-self) is not significantly different from zero, b = 0.032, p = 0.231.
7.2.2.1.3 Conscientiousness: personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the conscientiousness change score (ideal-self) is not significantly different from zero, b = -0.036, p = 0.164.
7.2.2.1.4 Neuroticism: personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the neuroticism change score (ideal-self) is not significantly different from zero, b = 0, p = 0.991.
7.2.2.1.5 Openness: personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the openness change score (ideal-self) is not significantly different from zero, b = 0.01, p = 0.656.
7.2.2.2 Big Five facets
Run models for all facets with a template & loop:
Show the code
# create templates:# 1st, for facet-specific acceptance goalfacet_template_ppd_accept <-'facet_t1 =~ 1*ind1_t1 + lamb2*ind2_t1 + lamb3*ind3_t1 + lamb4*ind4_t1 # This specifies the measurement model for facet at T1facet_t2 =~ 1*ind1_t2 + lamb2*ind2_t2 + lamb3*ind3_t2 + lamb4*ind4_t2 # This specifies the measurement model for facet at T2 (with equality constraints)facet_t2 ~ 1*facet_t1 # This parameter regresses facet_t2 perfectly on facet_t1d_facet_1 =~ 1*facet_t2 # This defines the latent change score factor as measured perfectly by scores on facet_t2facet_t2 ~ 0*1 # This line constrains the intercept of facet_t2 to 0facet_t2 ~~ 0*facet_t2 # This fixes the variance of facet_t2 to 0d_facet_1 ~ 1 # This estimates the intercept of the change score facet_t1 ~ 1 # This estimates the intercept of facet_t1 d_facet_1 ~~ d_facet_1 # This estimates the variance of the change scores facet_t1 ~~ facet_t1 # This estimates the variance of facet_t1 facet_t1 ~ ppd # This estimates the moderation effect on personality at T1d_facet_1 ~ facet_t1 + ppd # This estimates the self-feedback parameter and the moderation effect on the change scoreind1_t1 ~~ ind1_t2 # This allows residual covariance on indicator X1 across T1 and T2ind2_t1 ~~ ind2_t2 # This allows residual covariance on indicator X2 across T1 and T2ind3_t1 ~~ ind3_t2 # This allows residual covariance on indicator X3 across T1 and T2ind4_t1 ~~ ind4_t2 # This allows residual covariance on indicator X4 across T1 and T2ind1_t1 ~~ res1*ind1_t1 # This allows residual variance on indicator X1 at T1 ind2_t1 ~~ res2*ind2_t1 # This allows residual variance on indicator X2 at T1ind3_t1 ~~ res3*ind3_t1 # This allows residual variance on indicator X3 at T1ind4_t1 ~~ res4*ind4_t1 # This allows residual variance on indicator X4 at T1ind1_t2 ~~ res1*ind1_t2 # This allows residual variance on indicator X1 at T2 ind2_t2 ~~ res2*ind2_t2 # This allows residual variance on indicator X2 at T2 ind3_t2 ~~ res3*ind3_t2 # This allows residual variance on indicator X3 at T2ind4_t2 ~~ res4*ind4_t2 # This allows residual variance on indicator X4 at T2ind1_t1 ~ 0*1 # This constrains the intercept of X1 to 0 at T1ind2_t1 ~ m2*1 # This estimates the intercept of X2 at T1ind3_t1 ~ m3*1 # This estimates the intercept of X3 at T1ind4_t1 ~ m4*1 # This estimates the intercept of X4 at T1ind1_t2 ~ 0*1 # This constrains the intercept of X1 to 0 at T2ind2_t2 ~ m2*1 # This estimates the intercept of X2 at T2ind3_t2 ~ m3*1 # This estimates the intercept of X3 at T2ind4_t2 ~ m4*1 # This estimates the intercept of X4 at T2ppd ~~ ppdppd ~ 1'# loop across 15 facetsfor (i in6:length(b5_vars)) { item_nrs = b5_vars[[i]][[1]] short_name =str_trunc(names(b5_vars)[i], 5, ellipsis ="")# use BFI version combined pre&post current items =paste0(bfi_versions[[6]], item_nrs) template_filled <-str_replace_all(facet_template_ppd_accept, c("facet"= short_name,"ind1"= items[1], "ind2"= items[2], "ind3"= items[3], "ind4"= items[4])) facet_model_fit <-lavaan(template_filled, data=df_sbsa_wide_pers_sa_ppd, estimator='mlr', fixed.x=FALSE, missing='fiml')eval(call("<-", as.name(paste0("mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_ppd")), template_filled))eval(call("<-", as.name(paste0("fit_mi_lcs_", short_name, "_", str_sub(names(bfi_versions)[6], 6), "_ppd")), facet_model_fit))}
7.2.2.2.1 Sociability - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the sociability change score (ideal-self) is not significantly different from zero, b = -0.009, p = 0.76.
7.2.2.2.2 Assertiveness - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the assertiveness change score (ideal-self) is not significantly different from zero, b = 0.019, p = 0.403.
7.2.2.2.3 Energy - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the energy change score (ideal-self) is not significantly different from zero, b = -0.012, p = 0.577.
7.2.2.2.4 Compassion - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the compassion change score (ideal-self) is not significantly different from zero, b = 0.009, p = 0.861.
7.2.2.2.5 Respectfulness - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the respectfulness change score (ideal-self) is not significantly different from zero, b = 0.027, p = 0.274.
7.2.2.2.6 Trust - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the trust change score (ideal-self) is not significantly different from zero, b = 0.003, p = 0.923.
7.2.2.2.7 Organization - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the organization change score (ideal-self) is not significantly different from zero, b = 0.017, p = 0.38.
7.2.2.2.8 Productiveness - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the productiveness change score (ideal-self) is not significantly different from zero, b = 0.025, p = 0.355.
7.2.2.2.9 Responsibility - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the responsibility change score (ideal-self) is not significantly different from zero, b = -0.031, p = 0.384.
7.2.2.2.10 Anxiety - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the anxiety change score (ideal-self) is not significantly different from zero, b = -0.012, p = 0.824.
7.2.2.2.11 Depression - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the depression change score (ideal-self) is not significantly different from zero, b = 0.009, p = 0.66.
7.2.2.2.12 Volatility - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the volatility change score (ideal-self) is not significantly different from zero, b = -0.011, p = 0.793.
7.2.2.2.13 Curiosity - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the curiosity change score (ideal-self) is not significantly different from zero, b = 0.026, p = 0.347.
7.2.2.2.14 Aesthetic - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the aesthetic change score (ideal-self) is not significantly different from zero, b = 0.002, p = 0.419.
7.2.2.2.15 Imagination - personal project dimensions as moderator of change
Results summary (ppd = personal project dimensions):
The moderation effect of personal project dimensions with the imagination change score (ideal-self) is not significantly different from zero, b = -0.041, p = 0.187.
We will explore c) whether there are stronger rates of change/acceptance on traits that were selected as change goals than those that were not.
TBD!
7.4 Manipulation check (d)
Does self-improvement intervene on current self trait change (but not ideal self)? Does self-acceptance intervene on ideal self trait change (but not current self)?
kable(df_manip_check_curr_unlist %>%mutate(outcome =rep(sort(names(b5_vars)), each=4), term =c(rep(c("Intercept", "time", "group", "time*group"), 20))) %>%rename(p =`Pr(>|t|)`, str_err =`Std. Error`, est = Estimate) %>%select(outcome, term, est, str_err, p) %>%mutate(sig =ifelse(p < .05, ifelse(p < .01, ifelse(p < .001, "***", "**"), "*"), " ")) %>%arrange(factor(outcome, levels = (names(b5_vars)))), # order by BFI traits, digits =3)
outcome
term
est
str_err
p
sig
extraversion
Intercept
2.796
0.038
0.000
***
extraversion
time
0.166
0.026
0.000
***
extraversion
group
0.001
0.053
0.984
extraversion
time*group
-0.026
0.037
0.488
agreeableness
Intercept
3.553
0.032
0.000
***
agreeableness
time
0.076
0.024
0.002
**
agreeableness
group
0.016
0.045
0.724
agreeableness
time*group
0.018
0.034
0.594
conscientiousness
Intercept
3.328
0.041
0.000
***
conscientiousness
time
0.151
0.024
0.000
***
conscientiousness
group
0.056
0.058
0.333
conscientiousness
time*group
-0.037
0.034
0.282
neuroticism
Intercept
3.306
0.045
0.000
***
neuroticism
time
-0.192
0.031
0.000
***
neuroticism
group
-0.023
0.063
0.717
neuroticism
time*group
-0.020
0.044
0.645
openness
Intercept
3.654
0.033
0.000
***
openness
time
0.082
0.023
0.000
***
openness
group
-0.048
0.046
0.293
openness
time*group
-0.003
0.032
0.934
sociability
Intercept
2.469
0.051
0.000
***
sociability
time
0.191
0.036
0.000
***
sociability
group
-0.023
0.072
0.753
sociability
time*group
-0.007
0.050
0.893
assertiveness
Intercept
2.855
0.046
0.000
***
assertiveness
time
0.122
0.035
0.000
***
assertiveness
group
0.024
0.065
0.713
assertiveness
time*group
-0.044
0.048
0.365
energy
Intercept
3.063
0.046
0.000
***
energy
time
0.188
0.039
0.000
***
energy
group
0.002
0.064
0.980
energy
time*group
-0.027
0.054
0.617
compassion
Intercept
3.670
0.039
0.000
***
compassion
time
0.052
0.036
0.146
compassion
group
0.031
0.055
0.567
compassion
time*group
0.013
0.050
0.790
respectfulness
Intercept
3.953
0.040
0.000
***
respectfulness
time
0.075
0.032
0.022
*
respectfulness
group
-0.029
0.056
0.603
respectfulness
time*group
0.005
0.045
0.905
trust
Intercept
3.035
0.044
0.000
***
trust
time
0.100
0.035
0.005
**
trust
group
0.043
0.061
0.481
trust
time*group
0.039
0.049
0.433
organization
Intercept
3.431
0.054
0.000
***
organization
time
0.125
0.036
0.001
***
organization
group
0.061
0.076
0.419
organization
time*group
-0.005
0.050
0.924
productiveness
Intercept
3.095
0.049
0.000
***
productiveness
time
0.197
0.033
0.000
***
productiveness
group
0.067
0.068
0.325
productiveness
time*group
-0.045
0.047
0.340
responsibility
Intercept
3.456
0.042
0.000
***
responsibility
time
0.128
0.032
0.000
***
responsibility
group
0.041
0.059
0.487
responsibility
time*group
-0.056
0.045
0.206
anxiety
Intercept
3.608
0.048
0.000
***
anxiety
time
-0.147
0.039
0.000
***
anxiety
group
-0.022
0.067
0.741
anxiety
time*group
-0.063
0.055
0.251
depression
Intercept
3.221
0.054
0.000
***
depression
time
-0.280
0.041
0.000
***
depression
group
-0.030
0.076
0.691
depression
time*group
0.035
0.057
0.535
volatility
Intercept
3.088
0.054
0.000
***
volatility
time
-0.147
0.042
0.000
***
volatility
group
-0.015
0.075
0.837
volatility
time*group
-0.036
0.058
0.538
curiosity
Intercept
3.771
0.040
0.000
***
curiosity
time
0.114
0.034
0.001
***
curiosity
group
-0.036
0.056
0.527
curiosity
time*group
-0.038
0.047
0.425
aesthetic
Intercept
3.574
0.044
0.000
***
aesthetic
time
0.045
0.034
0.192
aesthetic
group
-0.070
0.061
0.247
aesthetic
time*group
0.017
0.048
0.730
imagination
Intercept
3.618
0.043
0.000
***
imagination
time
0.089
0.033
0.008
**
imagination
group
-0.039
0.061
0.524
imagination
time*group
0.011
0.047
0.817
No significant group effects (higher trait level in self-improvement group at baseline) and also no significant differences in the changes over time in current-self trait levels (timeXgroup). Significant changes over time in current-self trait levels (time) for most domains/facets (independent of intervention group membership).
kable(df_manip_check_ideal_unlist %>%mutate(outcome =rep(sort(names(b5_vars)), each=4), term =c(rep(c("Intercept", "time", "group", "time*group"), 20))) %>%rename(p =`Pr(>|t|)`, str_err =`Std. Error`, est = Estimate) %>%select(outcome, term, est, str_err, p) %>%mutate(sig =ifelse(p < .05, ifelse(p < .01, ifelse(p < .001, "***", "**"), "*"), " ")) %>%arrange(factor(outcome, levels = (names(b5_vars)))), # order by BFI traits, digits =3)
outcome
term
est
str_err
p
sig
extraversion
Intercept
3.921
0.027
0.000
***
extraversion
time
-0.029
0.024
0.233
extraversion
group
-0.029
0.037
0.428
extraversion
time*group
0.004
0.034
0.918
agreeableness
Intercept
4.020
0.030
0.000
***
agreeableness
time
-0.011
0.025
0.662
agreeableness
group
-0.001
0.042
0.980
agreeableness
time*group
0.051
0.034
0.137
conscientiousness
Intercept
4.392
0.026
0.000
***
conscientiousness
time
0.026
0.024
0.270
conscientiousness
group
-0.001
0.036
0.977
conscientiousness
time*group
-0.007
0.033
0.832
neuroticism
Intercept
1.749
0.027
0.000
***
neuroticism
time
0.010
0.025
0.696
neuroticism
group
0.019
0.037
0.602
neuroticism
time*group
-0.053
0.035
0.127
openness
Intercept
4.104
0.029
0.000
***
openness
time
0.000
0.024
0.996
openness
group
-0.058
0.040
0.149
openness
time*group
0.009
0.034
0.781
sociability
Intercept
3.790
0.038
0.000
***
sociability
time
-0.086
0.033
0.009
**
sociability
group
-0.071
0.053
0.178
sociability
time*group
0.045
0.046
0.336
assertiveness
Intercept
3.843
0.037
0.000
***
assertiveness
time
-0.057
0.036
0.115
assertiveness
group
-0.026
0.051
0.605
assertiveness
time*group
0.000
0.050
0.995
energy
Intercept
4.130
0.034
0.000
***
energy
time
0.058
0.038
0.129
energy
group
0.008
0.047
0.866
energy
time*group
-0.033
0.053
0.539
compassion
Intercept
3.958
0.039
0.000
***
compassion
time
-0.002
0.039
0.955
compassion
group
-0.024
0.054
0.656
compassion
time*group
0.043
0.054
0.428
respectfulness
Intercept
4.369
0.032
0.000
***
respectfulness
time
0.002
0.030
0.959
respectfulness
group
0.004
0.045
0.930
respectfulness
time*group
0.035
0.042
0.399
trust
Intercept
3.733
0.039
0.000
***
trust
time
-0.030
0.036
0.407
trust
group
0.014
0.055
0.802
trust
time*group
0.077
0.050
0.127
organization
Intercept
4.408
0.031
0.000
***
organization
time
0.008
0.032
0.796
organization
group
-0.013
0.043
0.755
organization
time*group
0.051
0.045
0.253
productiveness
Intercept
4.514
0.030
0.000
***
productiveness
time
0.040
0.031
0.194
productiveness
group
-0.011
0.041
0.784
productiveness
time*group
-0.018
0.043
0.680
responsibility
Intercept
4.255
0.034
0.000
***
responsibility
time
0.032
0.032
0.311
responsibility
group
0.021
0.047
0.660
responsibility
time*group
-0.054
0.044
0.220
anxiety
Intercept
1.942
0.035
0.000
***
anxiety
time
0.028
0.040
0.485
anxiety
group
0.055
0.049
0.261
anxiety
time*group
-0.118
0.056
0.034
*
depression
Intercept
1.528
0.029
0.000
***
depression
time
-0.027
0.031
0.378
depression
group
0.014
0.041
0.736
depression
time*group
-0.002
0.043
0.967
volatility
Intercept
1.777
0.033
0.000
***
volatility
time
0.030
0.032
0.357
volatility
group
-0.010
0.046
0.826
volatility
time*group
-0.044
0.045
0.337
curiosity
Intercept
4.092
0.036
0.000
***
curiosity
time
0.016
0.034
0.634
curiosity
group
-0.065
0.051
0.199
curiosity
time*group
-0.015
0.047
0.757
aesthetic
Intercept
3.817
0.039
0.000
***
aesthetic
time
0.044
0.036
0.224
aesthetic
group
-0.011
0.054
0.834
aesthetic
time*group
-0.070
0.051
0.171
imagination
Intercept
4.404
0.034
0.000
***
imagination
time
-0.058
0.036
0.108
imagination
group
-0.096
0.048
0.044
*
imagination
time*group
0.113
0.050
0.024
*
No significant group effects (higher trait level in self-improvement group at baseline; except for imagination facet) and also no significant differences in the changes over time in ideal-self trait levels (timeXgroup; except for imagination and anxiety facets). No significant changes over time in ideal-self trait levels (time) for most domains/facets (independent of intervention group membership) with the exception of sociability.